Evaluation & the Health Professions最新文献

The COVID-19 crisis rapidly introduced telemedicine as the predominate modality to deliver healthcare however this change has not received attention in primary care settings and the health-related impacts are unknown. The study's objective was to explore the effects of physician-led synchronous telemedicine compared to face-to-face care delivered in the primary care setting on healthcare system use and attributes of primary care as reported in recent studies. We performed a comprehensive literature search in five databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, PsycInfo) and critical appraisal using the Joanna Briggs Institute tools. Of 6,247 studies identified, 157 studies underwent full text review, and 19 studies were included. Most studies were conducted in the U.S. (78.9%) and used video and telephone telemedicine (57.9%). An outcome-based qualitative description and narrative synthesis showed similar or fewer emergency department visits, hospital visits, and prescribing, and fewer diagnostic tests and imaging for telemedicine visits compared to face-to-face care. Our systematic review fills a gap in the literature on telemedicine in primary care settings however our results need to be interpreted cautiously given studies' susceptibility to selection bias, confounding, and limited applicability to other health systems and time periods.

Climate change presents critical challenges to public health, and healthcare professionals play a vital role in addressing its effects. The Climate and Health Tool (CHAT) assesses awareness, concern, motivation, and behaviors regarding climate change and health. This study aimed to adapt the CHAT into Turkish and evaluate its psychometric properties among healthcare professionals in Türkiye. A cross-sectional validation study was conducted with 496 participants using international adaptation standards, including forward-backward translation and cognitive interviews. Confirmatory factor analysis confirmed the original five-factor structure with excellent model fit (RMSEA = .038, CFI = .971). Internal consistency was high (Cronbach's α = .886), and convergent validity was supported through positive correlations with the Carbon Footprint Awareness Scale and Climate Change Worry Scale. Measurement invariance testing demonstrated equivalence across gender groups. The Turkish CHAT is a valid and reliable tool for assessing climate-health competencies in research, education, and clinical settings.

This study highlights the importance of quantitatively evaluating conversation turns (CTs) and speaking roles (SRs) in dyadic conversations between Mandarin-Chinese-speaking older adults with dementia (OADs) and healthy older adults (HOAs). Evaluating CTs and SRs provides comprehensive insights into how conversation processes and content are collaboratively co-constructed. Four mutually familiar dyads, each consisting of one OAD and one HOA, participated in five weekly 10-min conversations. Results revealed asymmetrical contributions in CTs, with HOAs assuming significantly more CTs to facilitate the conversation, but symmetrical contributions in SRs, indicating balanced involvement from both groups in shaping content. HOAs frequently took obligatory and nonobligatory CTs as the Animator or Author, supporting OADs in engaging meaningfully as the Author or Principal. These conversational behaviors reflect cultural values prevalent among Mandarin Chinese speakers, such as modesty, loyalty, and solidarity. Thus, integrating CT and SR evaluations provides valuable insights into conversational co-construction, particularly critical in dementia conversation research. However, findings are context-specific and culturally influenced and require cautious interpretation. In future research, these evaluative tools should be expanded to diverse populations and conversational contexts to enhance understanding of conversational dynamics across different cultures.

Learning communities (LCs) stimulate the learning and cross-sectoral collaboration that are essential in multidisciplinary challenges, such as healthy weight approaches (HWAs). Previous research on two multidisciplinary LCs about HWAs devised a causal loop diagram (CLD) identifying dynamics (i.e., mechanisms) that describe the functioning of LCs during their starting phase (first 6 months after LC start). As LCs are likely to develop over project phases, this study aimed to ascertain whether and how the LC system dynamics were perceived to develop over time. Qualitative interviews conducted with the LC members at a second juncture were processed to be able to update the initial CLD. The updated diagram indeed illustrated how the multidisciplinary LCs were perceived to develop. The CLD became more extensive and consequently further explained three aspects: the complexity and interrelatedness of group dynamics, gaining insights through exchange, and conditions to execute actions. To ensure adequate group dynamics, learning, and action execution, LC stakeholders can regularly use the CLD as a blueprint to identify solutions for LC bottlenecks, such as members limitedly arranging the LC jointly. Future research is needed to investigate whether such developments are caused by different LC project phases and/or by the number of LC meetings and reflections.

Increasing numbers of research studies are completed entirely online, often using "crowdsourcing" platforms to recruit participants, such as Amazon's Mechanical Turk (MTurk) or Prolific. Over time, crowdsourced data collection has faced multiple threats to validity including bots, inattention, and misrepresentation. Fortunately, research and development of quality control techniques has resulted in approaches to conducting credible research using crowdsourcing platforms. However, advances in large language models (LLMs), particularly computer-using agents (CUAs), such as OpenAI's "Operator," may pose novel threats to the validity of crowdsourced research data in the future. This brief report explores how and why that might be the case and encourages further discussion of this issue.

This study aimed to investigate the construct of burnout in counseling trainees using item response theory. The psychometric properties of the Maslach Burnout Inventory-General Survey for Students (MBI-GS(S)) were examined with a sample of counseling trainees (N = 246). Based on the graded response model, the results supported only the three-factor solution of counseling trainees' burnout and provided little evidence for general burnout as a potential construct. Specific item-level information from multidimensional item response theory analysis is also presented, including item discrimination parameters, item difficulty location parameters, test information, and reliability. Implications for assessing and addressing counseling trainees' burnout in terms of educating future mental health professionals are discussed.

This methodological study aimed to assess the validity and reliability of the Received Social Support Scale for Persons with Mental Illness (rSSS-MI), developed by Chronister et al. (2022), by adapting it into Turkish. The study consisted of 274 people who applied to the psychiatry outpatient clinic of a state hospital between April 2023 and April 2024. Data were collected using a Personal Information Form, the Received Social Support Scale for Persons with Mental Illness, and the Multidimensional Scale of Perceived Social Support. The Turkish version of the rSSS-MI demonstrated a 3-factor structure consisting of 21-items, and the items' factor loadings ranged from 0.82 to 0.96. The goodness of fit indices were χ2/df = 3.335, GFI = 0.823, NFI = 0.933, TLI = 0.944, IFI = 0.952, CFI = 0.952, RMSEA = 0.092, and SRMR = 0.024. Additionally, Cronbach's alpha coefficient of the rSSS-MI was calculated as 0.98, and item-total score correlation coefficients were between 0.80 and 0.92. These findings indicate that the Turkish version of the rSSS-MI is a valid and reliable measurement tool.

Although there is growing interest in assessing the quality of life (QoL) in mental health services, there is currently no specific measurement tool for mental disorders in Turkey. This study aimed to evaluate the psychometric features of the Turkish version of the Mental Health Quality of Life Questionnaire (MHQoL). This methodological study was conducted with 310 patients diagnosed with mental disorders between October 2022 and June 2023. The MHQoL includes a descriptive system (MHQoL-7D) of seven items representing distinct dimensions- self-image, independence, mood, relationships, daily activities, physical health, and future- alongside a visual analog scale measuring general psychological well-being (MHQoL-VAS). Turkish MHQoL yielded a one-factor structure with 7 items and factor loadings ranging from 0.426 to 0.727. Goodness-of-fit indexes of the MHQoL were found to be χ2/df = 1.212, SRMR = 0.026, RMSEA = 0.026, GFI = 0.985, AGFI = 0.969, CFI = 0.995, IFI = 0.995, and TLI = 0.991. Cronbach's alpha coefficient was measured at 0.792, with item-total correlation coefficients from 0.378 to 0.609. Turkish MHQoL is a reliable and valid tool for evaluating QoL in individuals with mental health challenges.

Single-group interrupted time-series analysis (ITSA) is a popular non-experimental study design in healthcare research. However, little guidance is available to inform the power requirements of ITSA studies under most common usages. We performed simulations to estimate the number of time periods (ranging from 10 to 100) required for percentage increases in level and trend (from baseline), to achieve statistical significance (p < 0.05, p < 0.01) with >80% and >90% power, when autocorrelation ranges from -0.90 to 0.90, and the intervention is introduced at 33%, 50% and 67% of the time series. Larger effect sizes were required for shorter studies, as well as with increasing autocorrelation, and when the intervention was introduced earlier or later than halfway in the time series. The required effect sizes were generally lower for estimating a change in the level of the time series as compared with the change in the trend, but the opposite was true when the number of time periods was larger. Simulations of studies with 10 time periods consistently produced unreliable estimates. The tables created from these analyses as well as a new community-contributed Stata package called POWER_ITSA will guide healthcare researchers in determining the most efficient way to achieve anticipated treatment effects in single-group ITSA studies.

Qualitative research methodologies offer critical contextual insights into community-engaged program evaluations, addressing limitations in solely quantitative approaches. While randomized controlled trials (RCTs) are inherently rigorous, their design does not consider nuanced contextual factors and lived experiences that are valuable to understanding a program's impact on community and health outcomes within diverse populations. In this theoretical discussion, we posit that qualitative methods, particularly when grounded in a Community-Based Participatory Research (CBPR) framework, foster trust and collaboration, yielding richer, more community-specific data to enhance program evaluation. We also provide evidence-based, practical consideration for conducting qualitative research for community-engaged program evaluations. We explore various qualitative approaches and their application in program evaluations. We highlight the iterative nature of qualitative data analysis and emphasize the importance of qualitative methodological rigor - including coding, triangulation, and member checking - to ensure the program evaluation's community value, credibility, and deeper contextual evaluation. Ultimately, we underscore the value of integrating qualitative methods throughout a program's lifecycle for comprehensive assessments, improved program effectiveness, and enhanced equity.