In 2001, the American Psychological Association (APA) noted in its publication manual that effect size calculations should be included in manuscripts submitted for publication. However, researchers utilizing single subject designs have not typically embraced the approach of any analyses beyond that of the traditional visual analysis (Marascuilo & Busk, 1988; Parsonson & Baer, 1977). In visual analysis of single subject data, researchers have examined data for three changes in the data: trend, variability, and level. Using trend analysis, researchers have examined the direction of the data for an increasing (i.e., upward) or decreasing (i.e., downward) trend. Researchers have also inspected for change in data variability or bounce. Finally, researchers have noted changes in level or mean performance. Recent trends in the field of education have resulted in an increased need to synthesize data sets from single subject studies. For example, the No Child Left Behind Act (NCLBA; 2001) brought considerable attention to the term evidence-based practice. As Odom and colleagues described (2005), some have claimed that only randomized experimental group designs are appropriate for demonstrating scientific evidence. This precluded single subject studies from being included in contributions of scientific evidence on effective intervention methods. However, others have noted that rigorous single subject research has much to contribute when determining scientific knowledge within the field (Horner, et al., 2005). In order to support the use of single subject research as evidence-based, a process of synthesizing single subject data is needed. Additionally, the Individuals with Disabilities Education Act (2004) mandated that teachers use strategies based on evidence based research. It would be tragic for teachers to utilize only teaching strategies proven with group design research; hence a second need to summarize data from single subject studies. Finally, researchers conducting meta-analyses or research syntheses have needed a method for interpreting and comparing intervention effectiveness of single subject studies. Researchers and practitioners in the field have tried to synthesize intervention research and effect sizes have been calculated on single subject data (e.g., Ma, 2006; Parker, Hagan-Burke, & Vannest, 2007; Wanzek, et al., 2006).Therefore, the purpose of this paper is to present the types of measures that may be used to describe intervention effects of single subject research designs. Strengths and limitations of each method will be described. Finally, a recommendation will be made to assist in determining which method should be used with which types of single subject data. Regression Approaches Allison and Gorman described the use of regression models to calculate effect sizes with single subject data (Allison & Gorman, 1993; Faith, Allison, & Gorman, 1996). In doing so, the dependent measure in the study (e.g., reading fluency or out of seat beha
{"title":"(Effect) Size Matters: And So Does the Calculation.","authors":"Melissa L. Olive, Jessica H. Franco","doi":"10.1037/H0100642","DOIUrl":"https://doi.org/10.1037/H0100642","url":null,"abstract":"In 2001, the American Psychological Association (APA) noted in its publication manual that effect size calculations should be included in manuscripts submitted for publication. However, researchers utilizing single subject designs have not typically embraced the approach of any analyses beyond that of the traditional visual analysis (Marascuilo & Busk, 1988; Parsonson & Baer, 1977). In visual analysis of single subject data, researchers have examined data for three changes in the data: trend, variability, and level. Using trend analysis, researchers have examined the direction of the data for an increasing (i.e., upward) or decreasing (i.e., downward) trend. Researchers have also inspected for change in data variability or bounce. Finally, researchers have noted changes in level or mean performance. Recent trends in the field of education have resulted in an increased need to synthesize data sets from single subject studies. For example, the No Child Left Behind Act (NCLBA; 2001) brought considerable attention to the term evidence-based practice. As Odom and colleagues described (2005), some have claimed that only randomized experimental group designs are appropriate for demonstrating scientific evidence. This precluded single subject studies from being included in contributions of scientific evidence on effective intervention methods. However, others have noted that rigorous single subject research has much to contribute when determining scientific knowledge within the field (Horner, et al., 2005). In order to support the use of single subject research as evidence-based, a process of synthesizing single subject data is needed. Additionally, the Individuals with Disabilities Education Act (2004) mandated that teachers use strategies based on evidence based research. It would be tragic for teachers to utilize only teaching strategies proven with group design research; hence a second need to summarize data from single subject studies. Finally, researchers conducting meta-analyses or research syntheses have needed a method for interpreting and comparing intervention effectiveness of single subject studies. Researchers and practitioners in the field have tried to synthesize intervention research and effect sizes have been calculated on single subject data (e.g., Ma, 2006; Parker, Hagan-Burke, & Vannest, 2007; Wanzek, et al., 2006).Therefore, the purpose of this paper is to present the types of measures that may be used to describe intervention effects of single subject research designs. Strengths and limitations of each method will be described. Finally, a recommendation will be made to assist in determining which method should be used with which types of single subject data. Regression Approaches Allison and Gorman described the use of regression models to calculate effect sizes with single subject data (Allison & Gorman, 1993; Faith, Allison, & Gorman, 1996). In doing so, the dependent measure in the study (e.g., reading fluency or out of seat beha","PeriodicalId":88717,"journal":{"name":"The behavior analyst today","volume":"10 1","pages":"5-10"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"58471343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. M. Callaghan, W. Follette, L. Ruckstuhl, Peter J. N. Linnerooth
Many researchers and clinicians believe that the therapeutic relationship is essential in bringing about clinical change. Empirical research to support this contention is scarce in part due to the difficulty of specifying and measuring theoretically derived mechanisms of change and the important dimensions of the client-therapist relationship. Functional Analytic Psychotherapy (FAP; Kohlenberg & Tsai, 1991) is a behavioral treatment that delineates how the therapeutic relationship brings about clinical change in clear and measurable terms. While initial research has been conducted to demonstrating the effectiveness of FAP with different populations, the purported mechanism of clinical change in FAP has not been sufficiently documented. This study describes the creation of a behavioral coding system (the Functional Analytic Psychotherapy Rating Scale; FAPRS) to identify and specify the components believed to be essential in bringing about client behavior change in FAP. Interobserver agreement values indicated moderate to high levels of reliability for the coding system. Implications for future tests of FAP’s proposed mechanism of change and the validity of the coding system are discussed.
{"title":"The Functional Analytic Psychotherapy Rating Scale (FAPRS): A Behavioral Psychotherapy Coding System","authors":"G. M. Callaghan, W. Follette, L. Ruckstuhl, Peter J. N. Linnerooth","doi":"10.1037/H0100648","DOIUrl":"https://doi.org/10.1037/H0100648","url":null,"abstract":"Many researchers and clinicians believe that the therapeutic relationship is essential in bringing about clinical change. Empirical research to support this contention is scarce in part due to the difficulty of specifying and measuring theoretically derived mechanisms of change and the important dimensions of the client-therapist relationship. Functional Analytic Psychotherapy (FAP; Kohlenberg & Tsai, 1991) is a behavioral treatment that delineates how the therapeutic relationship brings about clinical change in clear and measurable terms. While initial research has been conducted to demonstrating the effectiveness of FAP with different populations, the purported mechanism of clinical change in FAP has not been sufficiently documented. This study describes the creation of a behavioral coding system (the Functional Analytic Psychotherapy Rating Scale; FAPRS) to identify and specify the components believed to be essential in bringing about client behavior change in FAP. Interobserver agreement values indicated moderate to high levels of reliability for the coding system. Implications for future tests of FAP’s proposed mechanism of change and the validity of the coding system are discussed.","PeriodicalId":88717,"journal":{"name":"The behavior analyst today","volume":"9 1","pages":"98-116"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"58471704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Initially, when we thought of doing this article, the first author (JC) felt distain. I (JC) must admit that I am not much of a prognosticator. Skinner (1990) argued that we do not "know the future" but we know the past. We attempt to discriminate relevant variables in the present from the past and respond to them. For example, if the reader has an interest in robotics, neural networks (e.g., Thrun, & Mitchell, 1993), and operant conditioning (e.g., Thrun & Schwartz, 1995) models of behavioral development. The world has aging populations. Often this population experiences problems with mobility. Today, this problem is handled by giving them scooters. Scooters have a problem in that once you start using them, the mobility rarely returns and they are not very flexible as to the places that you can go. In the movie Forrest Gump, Forrest had bracers for his legs made from metal to help him walk. Given this set of learning experiences, one can easily suggest envision solving the problem of older people walking as the creation of a device like Forrest's braces only with an operant neural network based chip. When the person is younger (say early 50s), they are made a set of braces. They are instructed to wear the braces for a week or so. Through this wearing, the person's muscles train the chip as to the person's movement range and muscle reactions. The chip is stored for year and when the person is experiencing problems in mobility (maybe 80 or 90 years old), the bracers are taken from the closet, the chip placed back in, and put on the person in effect creating an exoskeleton. This will instantly help the person to walk but it is farm more helpful then that- it can help the person to regain the strength to be independent again. The device can be designed so that each day of consecutive wearing, it gradually transfer .02% of the workload back to the existing muscle structures of the person who is the wearer. Thus, in a year or so, it gradually rebuilds the muscle to walk without the device. The above sounds like a plan, well maybe or maybe not. Lots of environmental variables might render the device worthless. For example, biological research on stem cells might develop to the point of recreating muscle tissue rapidly regenerating the lost muscle. Another possible break to the plan would be that since scooter technology already exists, it has many people working on its improvement as a technology to increase mobility, as opposed to our suggested exoskeleton. Finally, some unforeseen advance in some other field could change the landscape even further relegating our work to worthless. Like with the above an ever shifting environment and stimuli emerge, rarely can we make predictions with 100% certainty. The same can be said to be true for licensing. Is it a breath of new air for behavior analysts or a threat and bringer of doom? What we can predict is the community's reaction. When a vague stimuli emerges, signal detection theorists (Greene & Swets, 1966
{"title":"Licensure as a postmodern hero.","authors":"Joseph D. Cautilli, Michael Weinberg","doi":"10.1037/H0100641","DOIUrl":"https://doi.org/10.1037/H0100641","url":null,"abstract":"Initially, when we thought of doing this article, the first author (JC) felt distain. I (JC) must admit that I am not much of a prognosticator. Skinner (1990) argued that we do not \"know the future\" but we know the past. We attempt to discriminate relevant variables in the present from the past and respond to them. For example, if the reader has an interest in robotics, neural networks (e.g., Thrun, & Mitchell, 1993), and operant conditioning (e.g., Thrun & Schwartz, 1995) models of behavioral development. The world has aging populations. Often this population experiences problems with mobility. Today, this problem is handled by giving them scooters. Scooters have a problem in that once you start using them, the mobility rarely returns and they are not very flexible as to the places that you can go. In the movie Forrest Gump, Forrest had bracers for his legs made from metal to help him walk. Given this set of learning experiences, one can easily suggest envision solving the problem of older people walking as the creation of a device like Forrest's braces only with an operant neural network based chip. When the person is younger (say early 50s), they are made a set of braces. They are instructed to wear the braces for a week or so. Through this wearing, the person's muscles train the chip as to the person's movement range and muscle reactions. The chip is stored for year and when the person is experiencing problems in mobility (maybe 80 or 90 years old), the bracers are taken from the closet, the chip placed back in, and put on the person in effect creating an exoskeleton. This will instantly help the person to walk but it is farm more helpful then that- it can help the person to regain the strength to be independent again. The device can be designed so that each day of consecutive wearing, it gradually transfer .02% of the workload back to the existing muscle structures of the person who is the wearer. Thus, in a year or so, it gradually rebuilds the muscle to walk without the device. The above sounds like a plan, well maybe or maybe not. Lots of environmental variables might render the device worthless. For example, biological research on stem cells might develop to the point of recreating muscle tissue rapidly regenerating the lost muscle. Another possible break to the plan would be that since scooter technology already exists, it has many people working on its improvement as a technology to increase mobility, as opposed to our suggested exoskeleton. Finally, some unforeseen advance in some other field could change the landscape even further relegating our work to worthless. Like with the above an ever shifting environment and stimuli emerge, rarely can we make predictions with 100% certainty. The same can be said to be true for licensing. Is it a breath of new air for behavior analysts or a threat and bringer of doom? What we can predict is the community's reaction. When a vague stimuli emerges, signal detection theorists (Greene & Swets, 1966","PeriodicalId":88717,"journal":{"name":"The behavior analyst today","volume":"9 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"58471269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Functional Analytic Psychotherapy Rating Scale (FAPRS) is behavioral coding system designed to capture those essential client and therapist behaviors that occur during Functional Analytic Psychotherapy (FAP). The FAPRS manual presents the purpose and rules for documenting essential aspects of FAP. The FAPRS codes are exclusive and exhaustive for FAP essential behaviors but also include codes for generally effective therapy behaviors by both client and therapist. Client behaviors identified include those that are FAP-specific such as Clinically Relevant Behaviors (in-session improvements and problems), specification of controlling variables, and discussion of outside problems and improvements that have been identified as targeted behaviors. Therapist behaviors that have been identified as theoretically essential for conducing FAP are included such as discussions about the therapeutic relationship, responding effectively and ineffectively to in-session client behaviors, and evoking client behavior in-session. For each behavioral code a definition is provided along with examples and counter examples of how the code might be applied to client or therapist behaviors. A decision hierarchy is provided for those cases when a client or therapist behavioral event (called a turn) may receive more than one possible code. The FAPRS can be used as a tool in research (e.g., to provide evidence for the proposed mechanism of change for FAP) or as a method for assisting the training of psychotherapists. The FAPRS has demonstrated acceptable psychometric properties (demonstrated by Callaghan, Follette, Ruckstuhl, & Linnerooth, this issue).
{"title":"FAPRS Manual: Manual for the Functional Analytic Psychotherapy Rating Scale","authors":"G. M. Callaghan, W. Follette","doi":"10.1037/H0100649","DOIUrl":"https://doi.org/10.1037/H0100649","url":null,"abstract":"The Functional Analytic Psychotherapy Rating Scale (FAPRS) is behavioral coding system designed to capture those essential client and therapist behaviors that occur during Functional Analytic Psychotherapy (FAP). The FAPRS manual presents the purpose and rules for documenting essential aspects of FAP. The FAPRS codes are exclusive and exhaustive for FAP essential behaviors but also include codes for generally effective therapy behaviors by both client and therapist. Client behaviors identified include those that are FAP-specific such as Clinically Relevant Behaviors (in-session improvements and problems), specification of controlling variables, and discussion of outside problems and improvements that have been identified as targeted behaviors. Therapist behaviors that have been identified as theoretically essential for conducing FAP are included such as discussions about the therapeutic relationship, responding effectively and ineffectively to in-session client behaviors, and evoking client behavior in-session. For each behavioral code a definition is provided along with examples and counter examples of how the code might be applied to client or therapist behaviors. A decision hierarchy is provided for those cases when a client or therapist behavioral event (called a turn) may receive more than one possible code. The FAPRS can be used as a tool in research (e.g., to provide evidence for the proposed mechanism of change for FAP) or as a method for assisting the training of psychotherapists. The FAPRS has demonstrated acceptable psychometric properties (demonstrated by Callaghan, Follette, Ruckstuhl, & Linnerooth, this issue).","PeriodicalId":88717,"journal":{"name":"The behavior analyst today","volume":"9 1","pages":"57-97"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"58471787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Approximately 50% of individuals in inpatient substance abuse treatment centers will also meet criteria for comorbid PTSD (Brown et al., 1999). This combination of disorders has severe consequences for the individual in terms of course, symptom severity, and effectiveness of treatment. When working with a PTSD-SA population, there are several forms of substances which are more likely to be abused when compared to substance users that do not meet criteria for PTSD. Furthermore, these substances appear to be related to the specific symptoms pattern exhibited by the individual (Stewart, Conrod, Pihl, & Dongier, 1999). Research also indicates that some symptoms of PTSD are more likely than others to elicit substance use in general (Sharkansky, Brief, Peirce, Meehan, & Mannix, 1999). Additionally, the negative impact that substance use relapse risk situations have on an individual may further interfere with the individual's ability to cope effectively with the symptoms of PTSD, which would lead to an increase in both PTSD and SA symptoms/behaviors (Sharkansky et al., 1999). The combination of PTSD-SA also poses several barriers to effective treatment. Some of these barriers are based on clinical lore, and have not undergone the rigorous empirical testing pivotal in the field of psychology. Other barriers have been supported in the empirical field, and these must be addressed in order for treatment to be effective. Post-traumatic Stress Disorder Post-traumatic Stress Disorder (PTSD) was first recognized by the American Psychiatric Association as a diagnosable condition in 1980 when it was introduced into the Diagnostic and Statistical Manual of Mental Disorders, Third Edition (DMS-III) (American Psychiatric Association, 1980). Since that time, PTSD etiology, symptomology, and treatment have been extensively studied. PTSD is defined as the development of three categories of symptoms following exposure to a traumatic event in which the individual both (1) came into contact with an event that involved actual or threatened death or serious injury to self or others, and (2) responded to this event with intense fear, helplessness, or horror (American Psychiatric Association, 2000). In essence, exposure to a traumatic event is not sufficient to warrant a diagnosis of PTSD. The subjective, emotional experience of the individual in the aftermath of the trauma must also be taken into account (APA, 2000). The three clusters of symptoms that classify PTSD are reexperiencing, avoidance and numbing, and hyperarousal. Each of these symptom clusters is distinct and affects different areas of psychological functioning. Additionally, disturbances in each category can give rise to comorbid diagnoses associated with that cluster of symptoms that will further disrupt the individual's level of functioning (Taylor, 2006). Lastly, the DSM-IV-TR (2000) states that the symptoms must occur for a minimum of one month and cause clinically significant distress and impairment in sev
{"title":"TREATMENT OF PTSD AND SUBSTANCE ABUSE COMORBIDITY","authors":"Theresa Souza, C. Spates","doi":"10.1037/H0100643","DOIUrl":"https://doi.org/10.1037/H0100643","url":null,"abstract":"Approximately 50% of individuals in inpatient substance abuse treatment centers will also meet criteria for comorbid PTSD (Brown et al., 1999). This combination of disorders has severe consequences for the individual in terms of course, symptom severity, and effectiveness of treatment. When working with a PTSD-SA population, there are several forms of substances which are more likely to be abused when compared to substance users that do not meet criteria for PTSD. Furthermore, these substances appear to be related to the specific symptoms pattern exhibited by the individual (Stewart, Conrod, Pihl, & Dongier, 1999). Research also indicates that some symptoms of PTSD are more likely than others to elicit substance use in general (Sharkansky, Brief, Peirce, Meehan, & Mannix, 1999). Additionally, the negative impact that substance use relapse risk situations have on an individual may further interfere with the individual's ability to cope effectively with the symptoms of PTSD, which would lead to an increase in both PTSD and SA symptoms/behaviors (Sharkansky et al., 1999). The combination of PTSD-SA also poses several barriers to effective treatment. Some of these barriers are based on clinical lore, and have not undergone the rigorous empirical testing pivotal in the field of psychology. Other barriers have been supported in the empirical field, and these must be addressed in order for treatment to be effective. Post-traumatic Stress Disorder Post-traumatic Stress Disorder (PTSD) was first recognized by the American Psychiatric Association as a diagnosable condition in 1980 when it was introduced into the Diagnostic and Statistical Manual of Mental Disorders, Third Edition (DMS-III) (American Psychiatric Association, 1980). Since that time, PTSD etiology, symptomology, and treatment have been extensively studied. PTSD is defined as the development of three categories of symptoms following exposure to a traumatic event in which the individual both (1) came into contact with an event that involved actual or threatened death or serious injury to self or others, and (2) responded to this event with intense fear, helplessness, or horror (American Psychiatric Association, 2000). In essence, exposure to a traumatic event is not sufficient to warrant a diagnosis of PTSD. The subjective, emotional experience of the individual in the aftermath of the trauma must also be taken into account (APA, 2000). The three clusters of symptoms that classify PTSD are reexperiencing, avoidance and numbing, and hyperarousal. Each of these symptom clusters is distinct and affects different areas of psychological functioning. Additionally, disturbances in each category can give rise to comorbid diagnoses associated with that cluster of symptoms that will further disrupt the individual's level of functioning (Taylor, 2006). Lastly, the DSM-IV-TR (2000) states that the symptoms must occur for a minimum of one month and cause clinically significant distress and impairment in sev","PeriodicalId":88717,"journal":{"name":"The behavior analyst today","volume":"9 1","pages":"11-26"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"58471456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Ardoin, Claire M. Roof, Cynthia Klubnick, Jessica Carfolite
Curriculum-based measurement for reading (CBM-R) procedures were developed in the late 1970's and early 1980's as a set of standardized assessment tools for gauging students' academic performance in reading. It was developed in order to provide teachers with an efficient, easily understood measurement system yielding relevant data about students' level of performance, as well as their reading growth over time (Deno, 1985). Educators and researchers have noted that an important characteristic of CBM-R is its ability to measure both inter-individual differences in groups of students as well as intra-individual change within specific students (Fuchs & Fuchs, 1998; Fuchs, Fuchs, & Speece, 2002). While CBM-R data were initially used exclusively to guide low-stakes educational decisions (Deno, 1985; Deno, 1986; Deno, Marston, & Tindal, 1985; Deno & Shinn, 1989), CBM-R data are now being used for making high-stakes decisions (i.e., special education eligibility) within Response to Intervention (RtI) models. Several features distinguish CBM-R procedures from other standardized measures used to assess students' reading. First, the assessment materials are relatively cheap and it requires little time to administer probes to students. Second, CBM-R is meant to be a measurement of students' global reading performance, which allows for practitioners to evaluate how students' are progressing toward towards long-term goals (Deno, Fuchs, Marston, & Shin, 2001; Fuchs, Fuchs, Hamlett, Walz, & Germann, 1993). Finally, as described by Deno et al. "CBM-R departs from conventiona l psychometric applications by integrating the concepts of standardized measurement and traditional reliability and validity with features from behavioral and observational assessment methodology: repeated performance sampling, fixed time recording, graphic display of times-series data, and qualitative descriptions of performance" (Deno et al., 2001, p. 508) These characteristics makes CBM-R ideal for use within an RtI model, as an instrument used to both identify students at-risk for academic problems and to evaluate individual students' response to instruction. Within the Fuchs and Fuchs (1998) dual discrepancy RtI model, CBM-R data are first used to identify students who are at-risk for academic problems based upon a comparison of their performance to that of their normative group (i.e., nomothetic context). Such decisions are relatively low-stakes decisions, given that the identification of a student at-risk simply results in the student being provided with supplemental instruction. CBM-R data modeling individual student's response to the supplemental instruction is then used as a primary source of data for making high-stakes decisions. Students' response to supplemental instruction is generally evaluated using CBM-R progress monitoring procedures, which entails the frequent administration of CBM-R probes and plotting of collected data in time-series fashion. Progress monitoring data are
{"title":"Evaluating Curriculum-Based Measurement from a Behavioral Assessment Perspective.","authors":"S. Ardoin, Claire M. Roof, Cynthia Klubnick, Jessica Carfolite","doi":"10.1037/H0100646","DOIUrl":"https://doi.org/10.1037/H0100646","url":null,"abstract":"Curriculum-based measurement for reading (CBM-R) procedures were developed in the late 1970's and early 1980's as a set of standardized assessment tools for gauging students' academic performance in reading. It was developed in order to provide teachers with an efficient, easily understood measurement system yielding relevant data about students' level of performance, as well as their reading growth over time (Deno, 1985). Educators and researchers have noted that an important characteristic of CBM-R is its ability to measure both inter-individual differences in groups of students as well as intra-individual change within specific students (Fuchs & Fuchs, 1998; Fuchs, Fuchs, & Speece, 2002). While CBM-R data were initially used exclusively to guide low-stakes educational decisions (Deno, 1985; Deno, 1986; Deno, Marston, & Tindal, 1985; Deno & Shinn, 1989), CBM-R data are now being used for making high-stakes decisions (i.e., special education eligibility) within Response to Intervention (RtI) models. Several features distinguish CBM-R procedures from other standardized measures used to assess students' reading. First, the assessment materials are relatively cheap and it requires little time to administer probes to students. Second, CBM-R is meant to be a measurement of students' global reading performance, which allows for practitioners to evaluate how students' are progressing toward towards long-term goals (Deno, Fuchs, Marston, & Shin, 2001; Fuchs, Fuchs, Hamlett, Walz, & Germann, 1993). Finally, as described by Deno et al. \"CBM-R departs from conventiona l psychometric applications by integrating the concepts of standardized measurement and traditional reliability and validity with features from behavioral and observational assessment methodology: repeated performance sampling, fixed time recording, graphic display of times-series data, and qualitative descriptions of performance\" (Deno et al., 2001, p. 508) These characteristics makes CBM-R ideal for use within an RtI model, as an instrument used to both identify students at-risk for academic problems and to evaluate individual students' response to instruction. Within the Fuchs and Fuchs (1998) dual discrepancy RtI model, CBM-R data are first used to identify students who are at-risk for academic problems based upon a comparison of their performance to that of their normative group (i.e., nomothetic context). Such decisions are relatively low-stakes decisions, given that the identification of a student at-risk simply results in the student being provided with supplemental instruction. CBM-R data modeling individual student's response to the supplemental instruction is then used as a primary source of data for making high-stakes decisions. Students' response to supplemental instruction is generally evaluated using CBM-R progress monitoring procedures, which entails the frequent administration of CBM-R probes and plotting of collected data in time-series fashion. Progress monitoring data are ","PeriodicalId":88717,"journal":{"name":"The behavior analyst today","volume":"9 1","pages":"36-49"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"58471545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quantifying Social Learning Processes In the laboratory, paired demonstrator-observer designs are used primarily to investigate whether animals are capable of specific forms of social learning (Galef 1988). Laboratory experimental have also been used to investigate the diffusion dynamics of learned behaviour through populations in controlled conditions (Lefebvre & Palameta 1988; Whiten et al. 2005). The results of such experiments do not tell us much about social learning in the wild and often lack ecolo gical validity in terms of the behaviour being learned. They have the potential, however, to provide quantitative data that can be used in the parameters for mathematical models of social learning. For instance, Kendal et al. (2007) presented captive groups of callitrichid monkeys with novel extractive foraging tasks. By measuring the proximity of each monkey to the task and noting any food extractions, they quantified the effects of two social learning processes, ‘stimulus enhancement’ and ‘observational learning’, and two asocial processes, ‘intrinsic movement to the task’ and ‘asocial learning of the task’ on the adoption of a novel foraging behavior. The values of these processes from the observed data were fed into a set of models for the spread of a novel behaviour. Model selection was used to discern that the model best-fit to the monkey diffusion data only required asocial processes. Nonetheless, quantification of the processes provided statistical evidence for a small positive effect of stimulus enhancement, where a demonstrator manipulating the task attracts an observer to move to the task, but not for observational learning at the task. Derived parameter values can also be used in competing models to predict the shapes of diffusion curves. Theoretical models predict that the diffusion of cultural traits will typically exhibit a sigmoidal (‘S’ shaped) pattern over time (Boyd & Richerson 1985; Cavalli-Sforza & Feldman 1981), while asocial learning has been expected to result in a linear, non-acceleratory, or at least non-sigmoidal increase in frequency (Lefebvre 1995). In the absence of parameter values to feed into diffusion models, the observed shape of a diffusion curve is unlikely to be reliable, as more recent models predict that under certain conditions asocial learning can generate acceleratory curves while social learning can generate deceleratory curves (Laland & Kendal 2003; Reader 2004). Many competing sets of assumptions can generate very similar diffusion curves using different parameter values. Thus, if parameter values can be estimated, it may be possible to select between competing hypotheses. Population-Level Homogeneity of Behaviour While social learning experiments in the laboratory may provide estimates of the limitations of social learning behaviour, an understanding of social learning will be incomplete without analysis of natural populations. Field translocation experiments have been performed to identify tradition
{"title":"Modelling Social Learning in Monkeys.","authors":"J. Kendal","doi":"10.1037/H0100647","DOIUrl":"https://doi.org/10.1037/H0100647","url":null,"abstract":"Quantifying Social Learning Processes In the laboratory, paired demonstrator-observer designs are used primarily to investigate whether animals are capable of specific forms of social learning (Galef 1988). Laboratory experimental have also been used to investigate the diffusion dynamics of learned behaviour through populations in controlled conditions (Lefebvre & Palameta 1988; Whiten et al. 2005). The results of such experiments do not tell us much about social learning in the wild and often lack ecolo gical validity in terms of the behaviour being learned. They have the potential, however, to provide quantitative data that can be used in the parameters for mathematical models of social learning. For instance, Kendal et al. (2007) presented captive groups of callitrichid monkeys with novel extractive foraging tasks. By measuring the proximity of each monkey to the task and noting any food extractions, they quantified the effects of two social learning processes, ‘stimulus enhancement’ and ‘observational learning’, and two asocial processes, ‘intrinsic movement to the task’ and ‘asocial learning of the task’ on the adoption of a novel foraging behavior. The values of these processes from the observed data were fed into a set of models for the spread of a novel behaviour. Model selection was used to discern that the model best-fit to the monkey diffusion data only required asocial processes. Nonetheless, quantification of the processes provided statistical evidence for a small positive effect of stimulus enhancement, where a demonstrator manipulating the task attracts an observer to move to the task, but not for observational learning at the task. Derived parameter values can also be used in competing models to predict the shapes of diffusion curves. Theoretical models predict that the diffusion of cultural traits will typically exhibit a sigmoidal (‘S’ shaped) pattern over time (Boyd & Richerson 1985; Cavalli-Sforza & Feldman 1981), while asocial learning has been expected to result in a linear, non-acceleratory, or at least non-sigmoidal increase in frequency (Lefebvre 1995). In the absence of parameter values to feed into diffusion models, the observed shape of a diffusion curve is unlikely to be reliable, as more recent models predict that under certain conditions asocial learning can generate acceleratory curves while social learning can generate deceleratory curves (Laland & Kendal 2003; Reader 2004). Many competing sets of assumptions can generate very similar diffusion curves using different parameter values. Thus, if parameter values can be estimated, it may be possible to select between competing hypotheses. Population-Level Homogeneity of Behaviour While social learning experiments in the laboratory may provide estimates of the limitations of social learning behaviour, an understanding of social learning will be incomplete without analysis of natural populations. Field translocation experiments have been performed to identify tradition","PeriodicalId":88717,"journal":{"name":"The behavior analyst today","volume":"137 1","pages":"50-56"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"58471601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, behavior analysts have shown considerable interest in the burgeoning research area of derived stimulus relations which, many argue, may provide the foundations for a behavioral account of novel, complex behavior. Typically studied using a matching-to-sample (MTS) procedure, the basic finding is as follows. Suppose, for instance, reinforcement is delivered for selection of comparison B in the presence of the sample A, and for selection of comparison C in the presence of sample B, respectively. Most verbally able humans will now readily reverse these explicitly reinforced conditional discriminations in the absence of further training. That is, they will now select A given B, and B given C in accordance with derived symmetrical, or mutually entailed, stimulus relations. Furthermore, participants will now also select C given A and A given C in accordance with derived transitive and equivalence, or combinatorially entailed, stimulus relations without further training. Following such derived performances, the stimuli are said to participate in an equivalence class (Sidman, 1994) or a relational frame of equivalence (Barnes, 1994; Hayes, Barnes-Holmes, & Roche, 2001). Perhaps what is most interesting about derived stimulus relations such as equivalence is that the test outcomes are not readily predicted from the traditional concept of conditional discrimination; neither A nor C has a direct history of differential reinforcement with regard to the other, and therefore neither stimulus should control selection of the other. Another feature of derived stimulus relations is the transfer of stimulus functions, which has also generated research interest due in part to its implications for understanding a wide range of complex, derived behavior. The transfer of stimulus functions occurs when the function of one stimulus in a derived relation alters the functions of another according to the derived relation between the two, without additional training (see Dymond & Rehfeldt, 2000). To date, the transfer of functions through equivalence relations has been demonstrated with discriminative (e.g., Barnes & Keenan, 1993; Grey & Barnes, 1996; Roche, Barnes-Holmes, Smeets, Barnes-Holmes, & McGeady, 2000; Wulfert & Hayes, 1988), self-discriminative (Dymond & Barnes, 1994, 1997, 1998), consequential (Hayes, Kohlenberg, & Hayes, 1991; Greenway, Dougher, & Wulfert, 1996), and respondent (Dougher Augustson, Markham, Greenway, & Wulfert, 1994; Roche & Barnes, 1997; Roche et al., 2000) stimulus functions in adults and children. For instance, Barnes and Keenan (1993) first trained participants on a series of related conditional discriminations in a MTS format (i.e., A1-B1, A1-C1, A2-B2, A2C2), and then explicitly trained high-rate and low-rate performances on a schedule task in the presence of the two B stimuli (i.e., B1 = low-rate, B2 = high-rate). Subsequently, the researchers demonstrated a transfer of discriminative control over the two types of schedule perform
{"title":"Towards a behavioral analysis of humor: Derived transfer of self-reported humor ratings.","authors":"S. Dymond, D. Ferguson","doi":"10.1037/H0100636","DOIUrl":"https://doi.org/10.1037/H0100636","url":null,"abstract":"Recently, behavior analysts have shown considerable interest in the burgeoning research area of derived stimulus relations which, many argue, may provide the foundations for a behavioral account of novel, complex behavior. Typically studied using a matching-to-sample (MTS) procedure, the basic finding is as follows. Suppose, for instance, reinforcement is delivered for selection of comparison B in the presence of the sample A, and for selection of comparison C in the presence of sample B, respectively. Most verbally able humans will now readily reverse these explicitly reinforced conditional discriminations in the absence of further training. That is, they will now select A given B, and B given C in accordance with derived symmetrical, or mutually entailed, stimulus relations. Furthermore, participants will now also select C given A and A given C in accordance with derived transitive and equivalence, or combinatorially entailed, stimulus relations without further training. Following such derived performances, the stimuli are said to participate in an equivalence class (Sidman, 1994) or a relational frame of equivalence (Barnes, 1994; Hayes, Barnes-Holmes, & Roche, 2001). Perhaps what is most interesting about derived stimulus relations such as equivalence is that the test outcomes are not readily predicted from the traditional concept of conditional discrimination; neither A nor C has a direct history of differential reinforcement with regard to the other, and therefore neither stimulus should control selection of the other. Another feature of derived stimulus relations is the transfer of stimulus functions, which has also generated research interest due in part to its implications for understanding a wide range of complex, derived behavior. The transfer of stimulus functions occurs when the function of one stimulus in a derived relation alters the functions of another according to the derived relation between the two, without additional training (see Dymond & Rehfeldt, 2000). To date, the transfer of functions through equivalence relations has been demonstrated with discriminative (e.g., Barnes & Keenan, 1993; Grey & Barnes, 1996; Roche, Barnes-Holmes, Smeets, Barnes-Holmes, & McGeady, 2000; Wulfert & Hayes, 1988), self-discriminative (Dymond & Barnes, 1994, 1997, 1998), consequential (Hayes, Kohlenberg, & Hayes, 1991; Greenway, Dougher, & Wulfert, 1996), and respondent (Dougher Augustson, Markham, Greenway, & Wulfert, 1994; Roche & Barnes, 1997; Roche et al., 2000) stimulus functions in adults and children. For instance, Barnes and Keenan (1993) first trained participants on a series of related conditional discriminations in a MTS format (i.e., A1-B1, A1-C1, A2-B2, A2C2), and then explicitly trained high-rate and low-rate performances on a schedule task in the presence of the two B stimuli (i.e., B1 = low-rate, B2 = high-rate). Subsequently, the researchers demonstrated a transfer of discriminative control over the two types of schedule perform","PeriodicalId":88717,"journal":{"name":"The behavior analyst today","volume":"8 1","pages":"500-511"},"PeriodicalIF":0.0,"publicationDate":"2007-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"58471086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Children with autism are characterized by deficits in communication and social skills. Teaching children with autism to mand with adults is a common practice in behavioral interventions and is listed as a necessary skill to be taught in many curricular sequences (Leaf & McEachin, 1999; Lovaas, 1981; 2003; Maurice, 1996; Maurice, Green, & Foxx, 2001; Sundberg & Partington, 1998). Most of these curricular sequences also list manding with peers as a target skill in the area of socialization (Leaf & McEachin, 1999; Maurice, 1996; Maurice, Green, & Foxx, 2001; & Sundberg & Partington, 1998). The designation of these curricular sequences to include manding with adults and manding with peers as separate targets leads one to assume that this skill does not generalize across people, but must instead be specifically taught in each domain. In contrast, when analyzing the social behavior of typically developing preschool children, Tremblay, Strain, Hendrickson, and Shores (1981) found that, on average, they exhibited one initiation toward a peer every two minutes in an unstructured setting. A later study obtained similar results, showing that non-disabled children initiated with each other an average of five times in a 10--minute session (McGrath, Bosch, Sullivan & Fuqua, 2003). Research investigating the social behavior of children with autism and developmental delays repeatedly indicates impairments in socialization, including initiations toward peers (Guralnick & Weinhouse, 1984; Pierce-Jordan & Lifter, 2005; Stone & Lemanek, 1990). Many studies have been successful in teaching children with autism to initiate toward their peers using a variety of strategies including the use of a tactile prompt (Shabani et al. 2002; Taylor & Levin, 1998;), script fading (Krantz & McClannahan, 1993), visual supports ( Johnston, Nelson, Evans, & Palazolo, 2003), and peer tutors (Goldstein, Kaczmarek, Pennington, & Shafer, 1992; & Pierce & Schreibman, 1995). Hancock and Kaiser (1996) specifically taught children to mand with their siblings during play and snack times. Similarly, Taylor, Hoch, Potter, Rodriguez, Spinnato, & Kalaigian, (2005) taught children to mand for preferred items with their peers during snack time. The participants in the Taylor et al. study all were reportedly able to mand for preferred items with adults, but did not mand with peers until specifically taught to do so. Skinner (1957) described the different verbal operants as functionally independent. Establishing one verbal operant does not automatically result in the appearance of another. A word with the same topography may serve several different functions, such as a discriminative function or a reinforcing function. For example, the word or object "drink" may function as a discriminative stimulus which evokes a listener's echoic or tact response, but it may also function by producing a reinforcer when an establishing operation is in effect. Lamarre and Holland (1985) were the first to demonstrate e
{"title":"Generalization of Mands in Children with Autism from Adults to Peers.","authors":"Melanie Pellecchia, P. Hineline","doi":"10.1037/H0100634","DOIUrl":"https://doi.org/10.1037/H0100634","url":null,"abstract":"Children with autism are characterized by deficits in communication and social skills. Teaching children with autism to mand with adults is a common practice in behavioral interventions and is listed as a necessary skill to be taught in many curricular sequences (Leaf & McEachin, 1999; Lovaas, 1981; 2003; Maurice, 1996; Maurice, Green, & Foxx, 2001; Sundberg & Partington, 1998). Most of these curricular sequences also list manding with peers as a target skill in the area of socialization (Leaf & McEachin, 1999; Maurice, 1996; Maurice, Green, & Foxx, 2001; & Sundberg & Partington, 1998). The designation of these curricular sequences to include manding with adults and manding with peers as separate targets leads one to assume that this skill does not generalize across people, but must instead be specifically taught in each domain. In contrast, when analyzing the social behavior of typically developing preschool children, Tremblay, Strain, Hendrickson, and Shores (1981) found that, on average, they exhibited one initiation toward a peer every two minutes in an unstructured setting. A later study obtained similar results, showing that non-disabled children initiated with each other an average of five times in a 10--minute session (McGrath, Bosch, Sullivan & Fuqua, 2003). Research investigating the social behavior of children with autism and developmental delays repeatedly indicates impairments in socialization, including initiations toward peers (Guralnick & Weinhouse, 1984; Pierce-Jordan & Lifter, 2005; Stone & Lemanek, 1990). Many studies have been successful in teaching children with autism to initiate toward their peers using a variety of strategies including the use of a tactile prompt (Shabani et al. 2002; Taylor & Levin, 1998;), script fading (Krantz & McClannahan, 1993), visual supports ( Johnston, Nelson, Evans, & Palazolo, 2003), and peer tutors (Goldstein, Kaczmarek, Pennington, & Shafer, 1992; & Pierce & Schreibman, 1995). Hancock and Kaiser (1996) specifically taught children to mand with their siblings during play and snack times. Similarly, Taylor, Hoch, Potter, Rodriguez, Spinnato, & Kalaigian, (2005) taught children to mand for preferred items with their peers during snack time. The participants in the Taylor et al. study all were reportedly able to mand for preferred items with adults, but did not mand with peers until specifically taught to do so. Skinner (1957) described the different verbal operants as functionally independent. Establishing one verbal operant does not automatically result in the appearance of another. A word with the same topography may serve several different functions, such as a discriminative function or a reinforcing function. For example, the word or object \"drink\" may function as a discriminative stimulus which evokes a listener's echoic or tact response, but it may also function by producing a reinforcer when an establishing operation is in effect. Lamarre and Holland (1985) were the first to demonstrate e","PeriodicalId":88717,"journal":{"name":"The behavior analyst today","volume":"8 1","pages":"483-491"},"PeriodicalIF":0.0,"publicationDate":"2007-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"58471048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The United States Coast Guard has been conducting fatigue studies on its aircrew to establish a shift schedule that enables around-the-clock alert launch capability utilizing a limited number of personnel. In an attempt to get a better understanding of the effects that shift work has on people, their health, their performance, and their families, it is necessary to look at the history of shift work and the circadian cycle. Throughout the remainder of this paper, the term "shift work" means working hours that do not fall within the standard definition of a workday, 8:00 am to 5:00 pm. History of Shift Work At the dawn of the Industrial Revolution, shift work took on a new meaning. Bright artificial lighting made it possible to keep the factory running and extend work hours far past sunset (Hamermesh, 1998). This resulted in many significant changes to the normal work schedule as people wanted to incorporate more leisure into their lives. Instead of working six to seven days a week for shorter periods of time, the weekend became longer and more popular. "Weekly work hours in the United States dropped sharply during the first 40 years of the 20th century, with a concomitant move away from Sunday, and then Saturday work" (Hamermesh, 1998, p.323). Shift work is essential in many different industries around the world. Some examples of these include the medical field, industrial manufacturing, law enforcement, fire fighting, customer service, and military operations. Although society has become dependent on shift work, there are many problems, which are caused by its use. Some of the most common problems associated with it are work quality, employee satisfaction, home and family life, and employee health. Disadvantages of Shift Work Performance Research indicates that the greatest number of studies on shift work have been done in the medical field. The frequent long shifts that doctors and nurses are subjected to in hospitals have been a controversial topic for many years. Approximately 60% of nurses work shifts in excess of nine hours (Burke, 2003). To make matters worse, it is common for interns and less experienced nurses to work the longer shifts. Although some hospitals limit the length of work shifts, they often allow their employees to work back-to-back shifts. A study was conducted in Canada to see if a shortened intervention schedule would have an impact on performance. The interns in the study traditionally worked an on-call schedule every third night, with extended shifts up to 28 hours in length (Bernstein & Etchells, 2005). The intervention schedule used shifts less than 24 hours. Over a four-week test period, the interns worked 63 hours per week as opposed to the 79 hours with the traditional schedule. Trained physician observers documented the number of mistakes that were made by the interns. "The rate of serious medical errors made by interns was 36% higher during the traditional schedule than during the intervention schedule" (Bernstein
{"title":"Establishing a Safe and Effective Shift Schedule","authors":"Harper L. Phillips, Peggy M. Houghton","doi":"10.1037/H0100638","DOIUrl":"https://doi.org/10.1037/H0100638","url":null,"abstract":"The United States Coast Guard has been conducting fatigue studies on its aircrew to establish a shift schedule that enables around-the-clock alert launch capability utilizing a limited number of personnel. In an attempt to get a better understanding of the effects that shift work has on people, their health, their performance, and their families, it is necessary to look at the history of shift work and the circadian cycle. Throughout the remainder of this paper, the term \"shift work\" means working hours that do not fall within the standard definition of a workday, 8:00 am to 5:00 pm. History of Shift Work At the dawn of the Industrial Revolution, shift work took on a new meaning. Bright artificial lighting made it possible to keep the factory running and extend work hours far past sunset (Hamermesh, 1998). This resulted in many significant changes to the normal work schedule as people wanted to incorporate more leisure into their lives. Instead of working six to seven days a week for shorter periods of time, the weekend became longer and more popular. \"Weekly work hours in the United States dropped sharply during the first 40 years of the 20th century, with a concomitant move away from Sunday, and then Saturday work\" (Hamermesh, 1998, p.323). Shift work is essential in many different industries around the world. Some examples of these include the medical field, industrial manufacturing, law enforcement, fire fighting, customer service, and military operations. Although society has become dependent on shift work, there are many problems, which are caused by its use. Some of the most common problems associated with it are work quality, employee satisfaction, home and family life, and employee health. Disadvantages of Shift Work Performance Research indicates that the greatest number of studies on shift work have been done in the medical field. The frequent long shifts that doctors and nurses are subjected to in hospitals have been a controversial topic for many years. Approximately 60% of nurses work shifts in excess of nine hours (Burke, 2003). To make matters worse, it is common for interns and less experienced nurses to work the longer shifts. Although some hospitals limit the length of work shifts, they often allow their employees to work back-to-back shifts. A study was conducted in Canada to see if a shortened intervention schedule would have an impact on performance. The interns in the study traditionally worked an on-call schedule every third night, with extended shifts up to 28 hours in length (Bernstein & Etchells, 2005). The intervention schedule used shifts less than 24 hours. Over a four-week test period, the interns worked 63 hours per week as opposed to the 79 hours with the traditional schedule. Trained physician observers documented the number of mistakes that were made by the interns. \"The rate of serious medical errors made by interns was 36% higher during the traditional schedule than during the intervention schedule\" (Bernstein","PeriodicalId":88717,"journal":{"name":"The behavior analyst today","volume":"8 1","pages":"528-535"},"PeriodicalIF":0.0,"publicationDate":"2007-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"58471123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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