Purpose: To evaluate the effectiveness of the education program developed based on the structural empowerment (SE) and psychological empowerment (PE) theories and flipped classroom model for the empowerment of new graduate nurses (NGNs).
Methods: The study was conducted between June 2021 and September 2023 in two phases: developing the education program to empower NGNs and evaluating its effectiveness. An education program consisting of two parts, online and face-to-face, was developed. The online part consists of eight modules implemented for two weeks. The face-to-face part was implemented for two days and included the in-class activities. NGNs were randomly assigned to the intervention group (n: 32) and control group (n: 32). An education program was applied to the intervention group, whereas the control group continued their routine orientation program. A range of outcome measures of SE, PE, and education programs' effectiveness were evaluated. Data were analyzed using descriptive, chi-squared, and t-tests.
Results: The study determined that the intervention and control groups showed homogeneous distribution in the pretest. A statistically significant difference was identified between the intervention and control groups regarding the mean scores of PE and SE three months following the implementation of the education program, and the total mean score of the intervention group was higher.
Conclusion: The education program developed to empower NGNs was a highly effective intervention in increasing nurses' perceptions of SE and PE. There is a need to carry out studies and activities to disseminate this program.
Clinical relevance: The findings of this study will guide educators, researchers, and administrators in future strategies and innovative programs for empowering NGNs.
{"title":"Evaluating the effectiveness of the education program developed for the empowerment of new graduate nurses: A randomized controlled trial.","authors":"Seda Sarıköse, Sevilay Şenol Çelik","doi":"10.1111/jnu.13041","DOIUrl":"https://doi.org/10.1111/jnu.13041","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the effectiveness of the education program developed based on the structural empowerment (SE) and psychological empowerment (PE) theories and flipped classroom model for the empowerment of new graduate nurses (NGNs).</p><p><strong>Design: </strong>Single-center, parallel-group, randomized controlled trial.</p><p><strong>Methods: </strong>The study was conducted between June 2021 and September 2023 in two phases: developing the education program to empower NGNs and evaluating its effectiveness. An education program consisting of two parts, online and face-to-face, was developed. The online part consists of eight modules implemented for two weeks. The face-to-face part was implemented for two days and included the in-class activities. NGNs were randomly assigned to the intervention group (n: 32) and control group (n: 32). An education program was applied to the intervention group, whereas the control group continued their routine orientation program. A range of outcome measures of SE, PE, and education programs' effectiveness were evaluated. Data were analyzed using descriptive, chi-squared, and t-tests.</p><p><strong>Results: </strong>The study determined that the intervention and control groups showed homogeneous distribution in the pretest. A statistically significant difference was identified between the intervention and control groups regarding the mean scores of PE and SE three months following the implementation of the education program, and the total mean score of the intervention group was higher.</p><p><strong>Conclusion: </strong>The education program developed to empower NGNs was a highly effective intervention in increasing nurses' perceptions of SE and PE. There is a need to carry out studies and activities to disseminate this program.</p><p><strong>Clinical relevance: </strong>The findings of this study will guide educators, researchers, and administrators in future strategies and innovative programs for empowering NGNs.</p>","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Danielle Scharp PhD, APRN, FNP-BC, Jiyoun Song PhD, APRN, AGACNP-BC, Mollie Hobensack PhD, RN, Mary Happel Palmer PhD, RN, FAAN, AGSF, Veronica Barcelona PhD, RN, PHNA-BC, Maxim Topaz PhD, RN, MA
<div> <section> <h3> Introduction</h3> <p>Little is known about the range and frequency of symptoms among older adult home healthcare patients with urinary incontinence, as this information is predominantly contained in clinical notes. Natural language processing can uncover symptom information among older adults with urinary incontinence to promote holistic, equitable care.</p> </section> <section> <h3> Design</h3> <p>We conducted a secondary analysis of cross-sectional data collected between January 1, 2015, and December 31, 2017, from the largest HHC agency in the Northeastern United States. We aimed to develop and test a natural language processing algorithm to extract symptom information from clinical notes for older adults with urinary incontinence and analyze differences in symptom documentation by race or ethnicity.</p> </section> <section> <h3> Methods</h3> <p>Symptoms were identified through expert clinician-driven Delphi survey rounds. We developed a natural language processing algorithm for symptom identification in clinical notes, examined symptom documentation frequencies, and analyzed differences in symptom documentation by race or ethnicity using chi-squared tests and logistic regression models.</p> </section> <section> <h3> Results</h3> <p>In total, 39,179 home healthcare episodes containing 1,098,419 clinical notes for 29,981 distinct patients were included. Nearly 40% of the sample represented racially or ethnically minoritized groups (i.e., 18% Black, 14% Hispanic, 7% Asian/Pacific Islander, 0.3% multi-racial, and 0.2% Native American). Based on expert clinician-driven Delphi survey rounds, the following symptoms were identified: anxiety, dizziness, constipation, syncope, tachycardia, urinary frequency/urgency, urinary hesitancy/retention, and vision impairment/blurred vision. The natural language processing algorithm achieved excellent performance (average precision of 0.92). Approximately 29% of home healthcare episodes had symptom information documented. Compared to home healthcare episodes for White patients, home healthcare episodes for Asian/Pacific Islander (odds ratio = 0.74, 95% confidence interval [0.67–0.80], <i>p</i> < 0.001), Black (odds ratio = 0.69, 95% confidence interval [0.64–0.73], <i>p</i> < 0.001), and Hispanic (odds ratio = 0.91, 95% confidence interval [0.85–0.97], <i>p</i> < 0.01) patients were less likely to have any symptoms documented in clinical notes.</p> </section> <section> <h3> Conclusion</h3> <p>We found multidimensional symptoms and differences in symptom documentation among a diverse coho
{"title":"Applying natural language processing to understand symptoms among older adult home healthcare patients with urinary incontinence","authors":"Danielle Scharp PhD, APRN, FNP-BC, Jiyoun Song PhD, APRN, AGACNP-BC, Mollie Hobensack PhD, RN, Mary Happel Palmer PhD, RN, FAAN, AGSF, Veronica Barcelona PhD, RN, PHNA-BC, Maxim Topaz PhD, RN, MA","doi":"10.1111/jnu.13038","DOIUrl":"10.1111/jnu.13038","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Little is known about the range and frequency of symptoms among older adult home healthcare patients with urinary incontinence, as this information is predominantly contained in clinical notes. Natural language processing can uncover symptom information among older adults with urinary incontinence to promote holistic, equitable care.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Design</h3>\u0000 \u0000 <p>We conducted a secondary analysis of cross-sectional data collected between January 1, 2015, and December 31, 2017, from the largest HHC agency in the Northeastern United States. We aimed to develop and test a natural language processing algorithm to extract symptom information from clinical notes for older adults with urinary incontinence and analyze differences in symptom documentation by race or ethnicity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Symptoms were identified through expert clinician-driven Delphi survey rounds. We developed a natural language processing algorithm for symptom identification in clinical notes, examined symptom documentation frequencies, and analyzed differences in symptom documentation by race or ethnicity using chi-squared tests and logistic regression models.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In total, 39,179 home healthcare episodes containing 1,098,419 clinical notes for 29,981 distinct patients were included. Nearly 40% of the sample represented racially or ethnically minoritized groups (i.e., 18% Black, 14% Hispanic, 7% Asian/Pacific Islander, 0.3% multi-racial, and 0.2% Native American). Based on expert clinician-driven Delphi survey rounds, the following symptoms were identified: anxiety, dizziness, constipation, syncope, tachycardia, urinary frequency/urgency, urinary hesitancy/retention, and vision impairment/blurred vision. The natural language processing algorithm achieved excellent performance (average precision of 0.92). Approximately 29% of home healthcare episodes had symptom information documented. Compared to home healthcare episodes for White patients, home healthcare episodes for Asian/Pacific Islander (odds ratio = 0.74, 95% confidence interval [0.67–0.80], <i>p</i> < 0.001), Black (odds ratio = 0.69, 95% confidence interval [0.64–0.73], <i>p</i> < 0.001), and Hispanic (odds ratio = 0.91, 95% confidence interval [0.85–0.97], <i>p</i> < 0.01) patients were less likely to have any symptoms documented in clinical notes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>We found multidimensional symptoms and differences in symptom documentation among a diverse coho","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":"57 1","pages":"152-164"},"PeriodicalIF":2.4,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11772115/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142734408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ahmed Hashem El-Monshed, Ahmed Loutfy, Haitham El-Boraie, Abdel-Hady El-Gilany, Sara Mohamed Fayed, Amina Elzeiny, Heba Emad El-Gazar, Ahmed Salah Ali, Mohamed Ali Zoromba
Background: Attention-deficit hyperactivity disorder (ADHD), affecting 5% of global children, presents inattention, hyperactivity, and impulsivity. Sleep problems, prevalent in 70% of cases, exacerbate long-term psychosocial challenges.
Aim: This study aimed to assess the efficacy of behavioral sleep intervention in alleviating sleep problems, enhancing quality of life (QoL), and reducing ADHD symptom severity among children diagnosed with ADHD.
Method: A randomized controlled trial was conducted from June 2022 to April 2023. The intervention group (n = 47) engaged in a hybrid approach, participating in both face-to-face and Zoom sessions, while the control group (n = 45) received standard care. Data collection involved administering the Children's Sleep Habits Questionnaire (CSHQ), Pediatric Quality of Life Inventory (PedsQL), and ADHD Rating Scale IV (ADHD-RS-IV).
Results: Significant enhancements were observed in CSHQ (p ≤ 0.001, effect size = 1.913), PedsQL (p ≤ 0.001, effect size = 1.862), and a notable reduction in ADHD-RS-IV (p ≤ 0.001, effect size = 1.548). These findings highlight the intervention's efficacy in improving sleep quality, QoL, and reducing ADHD symptomatology.
Conclusion: This trial contributes substantially to understanding behavioral sleep interventions' impact on ADHD-affected children, enhancing quality of life and ADHD symptomatology. The study highlights the importance of individualized care for optimal outcomes in children with ADHD.
Clinical relevance: The study's findings underscore the potential for integrating behavioral sleep interventions into routine nursing practices, offering a valuable approach to address sleep-related challenges in children with ADHD. By focusing on sleep improvement, nurses can contribute to enhancing overall well-being and emotional regulation in these children within the context of comprehensive healthcare.
{"title":"The efficacy of behavioral sleep intervention on sleep problems among children with attention-deficit hyperactivity disorder: A randomized controlled trial.","authors":"Ahmed Hashem El-Monshed, Ahmed Loutfy, Haitham El-Boraie, Abdel-Hady El-Gilany, Sara Mohamed Fayed, Amina Elzeiny, Heba Emad El-Gazar, Ahmed Salah Ali, Mohamed Ali Zoromba","doi":"10.1111/jnu.13037","DOIUrl":"https://doi.org/10.1111/jnu.13037","url":null,"abstract":"<p><strong>Background: </strong>Attention-deficit hyperactivity disorder (ADHD), affecting 5% of global children, presents inattention, hyperactivity, and impulsivity. Sleep problems, prevalent in 70% of cases, exacerbate long-term psychosocial challenges.</p><p><strong>Aim: </strong>This study aimed to assess the efficacy of behavioral sleep intervention in alleviating sleep problems, enhancing quality of life (QoL), and reducing ADHD symptom severity among children diagnosed with ADHD.</p><p><strong>Method: </strong>A randomized controlled trial was conducted from June 2022 to April 2023. The intervention group (n = 47) engaged in a hybrid approach, participating in both face-to-face and Zoom sessions, while the control group (n = 45) received standard care. Data collection involved administering the Children's Sleep Habits Questionnaire (CSHQ), Pediatric Quality of Life Inventory (PedsQL), and ADHD Rating Scale IV (ADHD-RS-IV).</p><p><strong>Results: </strong>Significant enhancements were observed in CSHQ (p ≤ 0.001, effect size = 1.913), PedsQL (p ≤ 0.001, effect size = 1.862), and a notable reduction in ADHD-RS-IV (p ≤ 0.001, effect size = 1.548). These findings highlight the intervention's efficacy in improving sleep quality, QoL, and reducing ADHD symptomatology.</p><p><strong>Conclusion: </strong>This trial contributes substantially to understanding behavioral sleep interventions' impact on ADHD-affected children, enhancing quality of life and ADHD symptomatology. The study highlights the importance of individualized care for optimal outcomes in children with ADHD.</p><p><strong>Clinical relevance: </strong>The study's findings underscore the potential for integrating behavioral sleep interventions into routine nursing practices, offering a valuable approach to address sleep-related challenges in children with ADHD. By focusing on sleep improvement, nurses can contribute to enhancing overall well-being and emotional regulation in these children within the context of comprehensive healthcare.</p>","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142717430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<div> <section> <h3> Aim</h3> <p>The aim of this study was to evaluate and compare artificial intelligence (AI)-based large language models (LLMs) (ChatGPT-3.5, Bing, and Bard) with human-based formulations in generating relevant clinical queries, using comprehensive methodological evaluations.</p> </section> <section> <h3> Methods</h3> <p>To interact with the major LLMs ChatGPT-3.5, Bing Chat, and Google Bard, scripts and prompts were designed to formulate PICOT (population, intervention, comparison, outcome, time) clinical questions and search strategies. Quality of the LLMs responses was assessed using a descriptive approach and independent assessment by two researchers. To determine the number of hits, PubMed, Web of Science, Cochrane Library, and CINAHL Ultimate search results were imported separately, without search restrictions, with the search strings generated by the three LLMs and an additional one by the expert. Hits from one of the scenarios were also exported for relevance evaluation. The use of a single scenario was chosen to provide a focused analysis. Cronbach's alpha and intraclass correlation coefficient (ICC) were also calculated.</p> </section> <section> <h3> Results</h3> <p>In five different scenarios, ChatGPT-3.5 generated 11,859 hits, Bing 1,376,854, Bard 16,583, and an expert 5919 hits. We then used the first scenario to assess the relevance of the obtained results. The human expert search approach resulted in 65.22% (56/105) relevant articles. Bing was the most accurate AI-based LLM with 70.79% (63/89), followed by ChatGPT-3.5 with 21.05% (12/45), and Bard with 13.29% (42/316) relevant hits. Based on the assessment of two evaluators, ChatGPT-3.5 received the highest score (<i>M</i> = 48.50; SD = 0.71). Results showed a high level of agreement between the two evaluators. Although ChatGPT-3.5 showed a lower percentage of relevant hits compared to Bing, this reflects the nuanced evaluation criteria, where the subjective evaluation prioritized contextual accuracy and quality over mere relevance.</p> </section> <section> <h3> Conclusion</h3> <p>This study provides valuable insights into the ability of LLMs to formulate PICOT clinical questions and search strategies. AI-based LLMs, such as ChatGPT-3.5, demonstrate significant potential for augmenting clinical workflows, improving clinical query development, and supporting search strategies. However, the findings also highlight limitations that necessitate further refinement and continued human oversight.</p> </section> <section>
{"title":"PICOT questions and search strategies formulation: A novel approach using artificial intelligence automation","authors":"Lucija Gosak PhD candidate, MSc, RN, Gregor Štiglic PhD, BSc, Lisiane Pruinelli PhD, MSc, RN, Dominika Vrbnjak PhD, MSc, RN","doi":"10.1111/jnu.13036","DOIUrl":"10.1111/jnu.13036","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Aim</h3>\u0000 \u0000 <p>The aim of this study was to evaluate and compare artificial intelligence (AI)-based large language models (LLMs) (ChatGPT-3.5, Bing, and Bard) with human-based formulations in generating relevant clinical queries, using comprehensive methodological evaluations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>To interact with the major LLMs ChatGPT-3.5, Bing Chat, and Google Bard, scripts and prompts were designed to formulate PICOT (population, intervention, comparison, outcome, time) clinical questions and search strategies. Quality of the LLMs responses was assessed using a descriptive approach and independent assessment by two researchers. To determine the number of hits, PubMed, Web of Science, Cochrane Library, and CINAHL Ultimate search results were imported separately, without search restrictions, with the search strings generated by the three LLMs and an additional one by the expert. Hits from one of the scenarios were also exported for relevance evaluation. The use of a single scenario was chosen to provide a focused analysis. Cronbach's alpha and intraclass correlation coefficient (ICC) were also calculated.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In five different scenarios, ChatGPT-3.5 generated 11,859 hits, Bing 1,376,854, Bard 16,583, and an expert 5919 hits. We then used the first scenario to assess the relevance of the obtained results. The human expert search approach resulted in 65.22% (56/105) relevant articles. Bing was the most accurate AI-based LLM with 70.79% (63/89), followed by ChatGPT-3.5 with 21.05% (12/45), and Bard with 13.29% (42/316) relevant hits. Based on the assessment of two evaluators, ChatGPT-3.5 received the highest score (<i>M</i> = 48.50; SD = 0.71). Results showed a high level of agreement between the two evaluators. Although ChatGPT-3.5 showed a lower percentage of relevant hits compared to Bing, this reflects the nuanced evaluation criteria, where the subjective evaluation prioritized contextual accuracy and quality over mere relevance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This study provides valuable insights into the ability of LLMs to formulate PICOT clinical questions and search strategies. AI-based LLMs, such as ChatGPT-3.5, demonstrate significant potential for augmenting clinical workflows, improving clinical query development, and supporting search strategies. However, the findings also highlight limitations that necessitate further refinement and continued human oversight.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 ","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":"57 1","pages":"5-16"},"PeriodicalIF":2.4,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11771709/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142711262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p><strong>Introduction: </strong>The World Health Organization defines quality of care as providing effective, evidence-based care, and avoiding harm. Low-value care provides little or no benefit to the patient, causes harm, and wastes limited resources. In 2017, shortly after the start of the International Choosing Wisely campaign, the first Dutch nursing "Do-not-do" list was published and has become a widely used practical tool for nurses working in daily practice. However, over the last years new guidelines are published. Therefore, an update of the list is necessary with an addition of high-value care recommendations as alternative care practices for low-value care.</p><p><strong>Design/methods: </strong>In this study, a combination of designs was used. First, we searched Dutch clinical practice guidelines for low-value or high-value care recommendations. All nursing care recommendations were assessed and specified to several healthcare sectors, including hospital care, district care, nursing home care, disability care, and mental health care. Second, a prioritization among nurses regarding low-value care recommendations was done by a cross-sectional survey for each healthcare sector.</p><p><strong>Results: </strong>In total, 66 low-value care recommendations were found, for example, "avoid unnecessary layers under the patient at risk of pressure ulcers" and "never flush the bladder to prevent urinary tract infection." Furthermore, 414 high-value care recommendations were selected, such as "use the Barthel Index to assess and to evaluate the degree of ADL independence" and "application of cold therapy may be considered for oncological patients with pain." In total, 539 nurses from all healthcare sectors prioritized the low-value care recommendations, resulting in a top five low-value care practices per healthcare sector. The top five low-value care recommendations differed per healthcare sector, although "do not use physical restraints in case of a delirium" was prioritized by four out of five sectors.</p><p><strong>Conclusions: </strong>Assessing low-value and high-value care recommendations for nurses will help and inspire nurses to deliver fundamental care for their patients. These initiatives regarding low-value and high-value care are essential to generate a culture of continuous quality improvement based on evidence. This is also essential to meeting the current challenges of the healthcare delivery system.</p><p><strong>Clinical relevance: </strong>This paper provides an update of low-value care recommendations for nurses based on Dutch guidelines from 2017 to 2023, specified to five healthcare sectors, including hospital care, district care, nursing home care, disability care and mental health care, with an accompanying prioritization of these low-value care recommendations to facilitate de-implementation. This paper provides a first overview of high-value care recommendations to reflect on and create alternative care practices for l
{"title":"Low-value and high-value care recommendations in nursing: A systematic assessment of clinical practice guidelines.","authors":"Wilmieke Bahlman-van Ooijen, Jeltje Giesen, Annick Bakker-Jacobs, Hester Vermeulen, Getty Huisman-de Waal","doi":"10.1111/jnu.13029","DOIUrl":"https://doi.org/10.1111/jnu.13029","url":null,"abstract":"<p><strong>Introduction: </strong>The World Health Organization defines quality of care as providing effective, evidence-based care, and avoiding harm. Low-value care provides little or no benefit to the patient, causes harm, and wastes limited resources. In 2017, shortly after the start of the International Choosing Wisely campaign, the first Dutch nursing \"Do-not-do\" list was published and has become a widely used practical tool for nurses working in daily practice. However, over the last years new guidelines are published. Therefore, an update of the list is necessary with an addition of high-value care recommendations as alternative care practices for low-value care.</p><p><strong>Design/methods: </strong>In this study, a combination of designs was used. First, we searched Dutch clinical practice guidelines for low-value or high-value care recommendations. All nursing care recommendations were assessed and specified to several healthcare sectors, including hospital care, district care, nursing home care, disability care, and mental health care. Second, a prioritization among nurses regarding low-value care recommendations was done by a cross-sectional survey for each healthcare sector.</p><p><strong>Results: </strong>In total, 66 low-value care recommendations were found, for example, \"avoid unnecessary layers under the patient at risk of pressure ulcers\" and \"never flush the bladder to prevent urinary tract infection.\" Furthermore, 414 high-value care recommendations were selected, such as \"use the Barthel Index to assess and to evaluate the degree of ADL independence\" and \"application of cold therapy may be considered for oncological patients with pain.\" In total, 539 nurses from all healthcare sectors prioritized the low-value care recommendations, resulting in a top five low-value care practices per healthcare sector. The top five low-value care recommendations differed per healthcare sector, although \"do not use physical restraints in case of a delirium\" was prioritized by four out of five sectors.</p><p><strong>Conclusions: </strong>Assessing low-value and high-value care recommendations for nurses will help and inspire nurses to deliver fundamental care for their patients. These initiatives regarding low-value and high-value care are essential to generate a culture of continuous quality improvement based on evidence. This is also essential to meeting the current challenges of the healthcare delivery system.</p><p><strong>Clinical relevance: </strong>This paper provides an update of low-value care recommendations for nurses based on Dutch guidelines from 2017 to 2023, specified to five healthcare sectors, including hospital care, district care, nursing home care, disability care and mental health care, with an accompanying prioritization of these low-value care recommendations to facilitate de-implementation. This paper provides a first overview of high-value care recommendations to reflect on and create alternative care practices for l","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Missed nursing care: Expanding the research scope for a comprehensive understanding","authors":"Yanling He, Xuerong Zhang","doi":"10.1111/jnu.13035","DOIUrl":"10.1111/jnu.13035","url":null,"abstract":"","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":"57 1","pages":"178"},"PeriodicalIF":2.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142647825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Leodoro J. Labrague RN, DM, PhD, CNE, Sulaiman Al Sabei RN, PhD, Raeda AbuAlRub RN, PhD, Ikram Burney MD, Omar Al Rawajfah RN, PhD, FAAN
{"title":"Response to a Letter to the Editor on “The Role of Nurses' Adherence to Clinical Safety Guidelines in Linking Nurse Practice Environment to Missed Nursing Care”","authors":"Leodoro J. Labrague RN, DM, PhD, CNE, Sulaiman Al Sabei RN, PhD, Raeda AbuAlRub RN, PhD, Ikram Burney MD, Omar Al Rawajfah RN, PhD, FAAN","doi":"10.1111/jnu.13034","DOIUrl":"10.1111/jnu.13034","url":null,"abstract":"","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":"57 1","pages":"179-180"},"PeriodicalIF":2.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142648780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Postoperative nausea and vomiting (PONV) is a common adverse event after general surgery. This study aimed to examine the effectiveness and safety of transcutaneous electrical acupoint stimulation (TEAS) for the prevention of nausea and vomiting after laparoscopic surgery.
Method: The Cochrane Library, Pubmed, Embase, and Web of Science databases were accessed from inception to 23 January 2024. The incidence of PONV was the primary outcome measure. The required information size (RIS) of each outcome was estimated by Trial sequential analysis (TSA). The RoB 2.0 tool was used to assess the risk of bias and GRADE to assess the quality of evidence.
Results: Seventeen RCTs including 3698 participants were included. In comparison to the control group, TEAS reduced the incidence of PONV (13 trials, n = 3310; RR, 0.56; 95% CI, 0.46-0.67; I2 = 64%; p < 0.01; RIS = 1100), with the level of evidence graded as low. TEAS reduced the incidence of PON (9 trials, n = 2762; RR, 0.64; 95% CI, 0.52-0.79; I2 = 57%, p < 0.01; RIS = 1595), and was also associated with a lower incidence of POV (9 trials, n = 2797; RR, 0.53; 95% CI, 0.45-0.63; I2 = 0%, p < 0.01; RIS = 773).
Conclusion: The current meta-analysis and TSA provide reliable evidence that TEAS is an effective and safe method to prevent PONV. It may reduce the workload of nursing professionals, alleviate emotional stress, and decrease exposure risk. Adverse events related to TEAS were mild.
Clinical relevance: Nurses can incorporate TEAS into the rehabilitation nursing of patients experiencing PONV.
{"title":"Transcutaneous electrical acupoint stimulation for preventing postoperative nausea and vomiting after laparoscopic surgery: A meta-analysis.","authors":"Lu Yuan, Si-Jie Quan, Xin-Yu Li, Bo-Zhu Chen, Yan-Bing Huang, Hui Zheng","doi":"10.1111/jnu.13033","DOIUrl":"https://doi.org/10.1111/jnu.13033","url":null,"abstract":"<p><strong>Background: </strong>Postoperative nausea and vomiting (PONV) is a common adverse event after general surgery. This study aimed to examine the effectiveness and safety of transcutaneous electrical acupoint stimulation (TEAS) for the prevention of nausea and vomiting after laparoscopic surgery.</p><p><strong>Method: </strong>The Cochrane Library, Pubmed, Embase, and Web of Science databases were accessed from inception to 23 January 2024. The incidence of PONV was the primary outcome measure. The required information size (RIS) of each outcome was estimated by Trial sequential analysis (TSA). The RoB 2.0 tool was used to assess the risk of bias and GRADE to assess the quality of evidence.</p><p><strong>Results: </strong>Seventeen RCTs including 3698 participants were included. In comparison to the control group, TEAS reduced the incidence of PONV (13 trials, n = 3310; RR, 0.56; 95% CI, 0.46-0.67; I<sup>2</sup> = 64%; p < 0.01; RIS = 1100), with the level of evidence graded as low. TEAS reduced the incidence of PON (9 trials, n = 2762; RR, 0.64; 95% CI, 0.52-0.79; I<sup>2</sup> = 57%, p < 0.01; RIS = 1595), and was also associated with a lower incidence of POV (9 trials, n = 2797; RR, 0.53; 95% CI, 0.45-0.63; I<sup>2</sup> = 0%, p < 0.01; RIS = 773).</p><p><strong>Conclusion: </strong>The current meta-analysis and TSA provide reliable evidence that TEAS is an effective and safe method to prevent PONV. It may reduce the workload of nursing professionals, alleviate emotional stress, and decrease exposure risk. Adverse events related to TEAS were mild.</p><p><strong>Clinical relevance: </strong>Nurses can incorporate TEAS into the rehabilitation nursing of patients experiencing PONV.</p>","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142632013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ita Daryanti Saragih, Ira Suarilah, Mulyadi Mulyadi, Ice Septriani Saragih, Bih-O Lee
Purpose: Pain is a frequent post-stroke health concern, and several non-pharmacological interventions are commonly employed to manage it. However, few reviews have examined the effectiveness of such interventions, making it difficult to draw conclusions about their usefulness. Furthermore, subgroup analysis based on post-stroke pain level or intervention characteristics is rarely performed. This study aimed to investigate the effectiveness of non-pharmacological interventions and evaluate the significant factors associated with post-stroke pain through subgroup analysis.
Design: Systematic review and meta-analysis.
Methods: Relevant studies were obtained from seven databases, from their commencement up to March 2024, as well as from the gray literature. The PICOS approach was used to evaluate the eligibility criteria of the studies. The RoB-2 tool was used to determine the risk of bias in each randomized trial. Pooled estimations of standardized mean difference and heterogeneity (quantified with I2) were obtained using a random-effects model. The stability of the pooled result was then assessed using the leave-one-out approach. STATA 17.0 was used to run the meta-analysis.
Findings: Non-pharmacological interventions were effective in reducing pain immediately after intervention (pooled SMDs: -0.79; 95% confidence interval [CI]: -1.06 to -0.53; p < 0.001). The approach involving acupuncture, aquatic therapy, or laser therapy and rehabilitation training was effective for post-stroke hemiplegic shoulder pain. A pooled analysis of non-pharmacological interventions showed that both less than 4 weeks and more than 4 weeks of interventions were effective in alleviating pain in stroke patients.
Conclusion: Non-pharmacological approaches appear to be beneficial for reducing post-stroke pain. The outcomes based on the modalities merit further research.
Clinical relevance: Further studies are needed to determine the effects of different modalities on pain intensity following a stroke. Furthermore, to avoid overestimation of intervention efficacy, future randomized trials should consider blinding approaches to the interventions delivered.
{"title":"Beneficial effects of non-pharmacological interventions for post-stroke pain: A meta-analysis.","authors":"Ita Daryanti Saragih, Ira Suarilah, Mulyadi Mulyadi, Ice Septriani Saragih, Bih-O Lee","doi":"10.1111/jnu.13032","DOIUrl":"https://doi.org/10.1111/jnu.13032","url":null,"abstract":"<p><strong>Purpose: </strong>Pain is a frequent post-stroke health concern, and several non-pharmacological interventions are commonly employed to manage it. However, few reviews have examined the effectiveness of such interventions, making it difficult to draw conclusions about their usefulness. Furthermore, subgroup analysis based on post-stroke pain level or intervention characteristics is rarely performed. This study aimed to investigate the effectiveness of non-pharmacological interventions and evaluate the significant factors associated with post-stroke pain through subgroup analysis.</p><p><strong>Design: </strong>Systematic review and meta-analysis.</p><p><strong>Methods: </strong>Relevant studies were obtained from seven databases, from their commencement up to March 2024, as well as from the gray literature. The PICOS approach was used to evaluate the eligibility criteria of the studies. The RoB-2 tool was used to determine the risk of bias in each randomized trial. Pooled estimations of standardized mean difference and heterogeneity (quantified with I<sup>2</sup>) were obtained using a random-effects model. The stability of the pooled result was then assessed using the leave-one-out approach. STATA 17.0 was used to run the meta-analysis.</p><p><strong>Findings: </strong>Non-pharmacological interventions were effective in reducing pain immediately after intervention (pooled SMDs: -0.79; 95% confidence interval [CI]: -1.06 to -0.53; p < 0.001). The approach involving acupuncture, aquatic therapy, or laser therapy and rehabilitation training was effective for post-stroke hemiplegic shoulder pain. A pooled analysis of non-pharmacological interventions showed that both less than 4 weeks and more than 4 weeks of interventions were effective in alleviating pain in stroke patients.</p><p><strong>Conclusion: </strong>Non-pharmacological approaches appear to be beneficial for reducing post-stroke pain. The outcomes based on the modalities merit further research.</p><p><strong>Clinical relevance: </strong>Further studies are needed to determine the effects of different modalities on pain intensity following a stroke. Furthermore, to avoid overestimation of intervention efficacy, future randomized trials should consider blinding approaches to the interventions delivered.</p>","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142607311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sena Chae, Anahita Davoudi, Jiyoun Song, Lauren Evans, Kathryn H. Bowles, Margaret V. Mcdonald, Yolanda Barrón, Se Hee Min PhD, RN, Sungho Oh PhD, Danielle Scharp MSN, RN, Zidu Xu MMed, BS, RN, Maxim Topaz
<div> <section> <h3> Background</h3> <p>The healthcare industry increasingly values high-quality and personalized care. Patients with heart failure (HF) receiving home health care (HHC) often experience hospitalizations due to worsening symptoms and comorbidities. Therefore, close symptom monitoring and timely intervention based on risk prediction could help HHC clinicians prevent emergency department (ED) visits and hospitalizations. This study aims to (1) describe important variables associated with a higher risk of ED visits and hospitalizations in HF patients receiving HHC; (2) map data requirements of a clinical decision support (CDS) tool to the exchangeable data standard for integrating a CDS tool into the care of patients with HF; (3) outline a pipeline for developing a real-time artificial intelligence (AI)-based CDS tool.</p> </section> <section> <h3> Methods</h3> <p>We used patient data from a large HHC organization in the Northeastern US to determine the factors that can predict ED visits and hospitalizations among patients with HF in HHC (9362 patients in 12,223 care episodes). We examined vital signs, HHC visit details (e.g., the purpose of the visit), and clinical note–derived variables. The study identified critical factors that can predict ED visits and hospitalizations and used these findings to suggest a practical CDS tool for nurses. The tool's proposed design includes a system that can analyze data quickly to offer timely advice to healthcare clinicians.</p> </section> <section> <h3> Results</h3> <p>Our research showed that the length of time since a patient was admitted to HHC and how recently they have shown symptoms of HF were significant factors predicting an adverse event. Additionally, we found this information from the last few HHC visits before the occurrence of an ED visit or hospitalization were particularly important in the prediction. One hundred percent of clinical demographic profiles from the Outcome and Assessment Information Set variables were mapped to the exchangeable data standard, while natural language processing–driven variables couldn't be mapped due to their nature, as they are generated from unstructured data. The suggested CDS tool alerts nurses about newly emerging or rising risks, helping them make informed decisions.</p> </section> <section> <h3> Conclusions</h3> <p>This study discusses the creation of a time-series risk prediction model and its potential CDS applications within HHC, aiming to enhance patient outcomes, streamline resource utilization, and improve the quality of care for individuals
{"title":"Developing a clinical decision support framework for integrating predictive models into routine nursing practices in home health care for patients with heart failure","authors":"Sena Chae, Anahita Davoudi, Jiyoun Song, Lauren Evans, Kathryn H. Bowles, Margaret V. Mcdonald, Yolanda Barrón, Se Hee Min PhD, RN, Sungho Oh PhD, Danielle Scharp MSN, RN, Zidu Xu MMed, BS, RN, Maxim Topaz","doi":"10.1111/jnu.13030","DOIUrl":"10.1111/jnu.13030","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The healthcare industry increasingly values high-quality and personalized care. Patients with heart failure (HF) receiving home health care (HHC) often experience hospitalizations due to worsening symptoms and comorbidities. Therefore, close symptom monitoring and timely intervention based on risk prediction could help HHC clinicians prevent emergency department (ED) visits and hospitalizations. This study aims to (1) describe important variables associated with a higher risk of ED visits and hospitalizations in HF patients receiving HHC; (2) map data requirements of a clinical decision support (CDS) tool to the exchangeable data standard for integrating a CDS tool into the care of patients with HF; (3) outline a pipeline for developing a real-time artificial intelligence (AI)-based CDS tool.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We used patient data from a large HHC organization in the Northeastern US to determine the factors that can predict ED visits and hospitalizations among patients with HF in HHC (9362 patients in 12,223 care episodes). We examined vital signs, HHC visit details (e.g., the purpose of the visit), and clinical note–derived variables. The study identified critical factors that can predict ED visits and hospitalizations and used these findings to suggest a practical CDS tool for nurses. The tool's proposed design includes a system that can analyze data quickly to offer timely advice to healthcare clinicians.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our research showed that the length of time since a patient was admitted to HHC and how recently they have shown symptoms of HF were significant factors predicting an adverse event. Additionally, we found this information from the last few HHC visits before the occurrence of an ED visit or hospitalization were particularly important in the prediction. One hundred percent of clinical demographic profiles from the Outcome and Assessment Information Set variables were mapped to the exchangeable data standard, while natural language processing–driven variables couldn't be mapped due to their nature, as they are generated from unstructured data. The suggested CDS tool alerts nurses about newly emerging or rising risks, helping them make informed decisions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study discusses the creation of a time-series risk prediction model and its potential CDS applications within HHC, aiming to enhance patient outcomes, streamline resource utilization, and improve the quality of care for individuals","PeriodicalId":51091,"journal":{"name":"Journal of Nursing Scholarship","volume":"57 1","pages":"165-177"},"PeriodicalIF":2.4,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11771534/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142607312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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