Tag: statistics

Womens Health (Lond). 2026 Jan-Dec;22:17455057261476574. doi: 10.1177/17455057261476574. Epub 2026 Aug 10.

ABSTRACT

BackgroundMaternal depression during pregnancy is a major global health concern that affects both mothers and infants. However, limited studies have examined maternal depression across pregnancy stages in Arabic-speaking populations, where fertility rates are high.ObjectivesThis study aims to evaluate the relationship between antenatal depression and anxiety symptoms during the early (8-12 weeks) and later stages (24-28 weeks) of pregnancy and their effects on maternal and neonatal outcomes among Omani women.DesignProspective cohort study.MethodsA prospective cohort design was used, involving 302 pregnant Omani women receiving antenatal care at Al Buraimi Hospital. Eligible participants were aged 18-45 years, between 8-12 weeks of gestation and expected to continue care at the same clinic. Depression and anxiety were measured at both stages using the Arabic version of the Edinburgh Postnatal Depression Scale (EPDS) and the EPDS-3A subscale. Statistical analyses, including chi-square tests and logistic regression, were used to examine associations between anxiety, depression, and pregnancy outcomes. Multiple regression analyses controlled for maternal age, marital status, parity, pre-pregnancy BMI, and household income.ResultsHigh levels of depressive and anxiety symptoms were identified, particularly as pregnancy advanced. Among 302 pregnant Omani women, the prevalence of depressive symptoms was 29.8% and anxiety symptoms was 24.8% in early pregnancy. Women with elevated EPDS scores had higher risks of caesarean delivery, low birth weight, and preterm birth. Elevated anxiety was associated with greater maternal distress and poorer neonatal outcomes.ConclusionFindings highlight the importance of integrating mental health screening into routine antenatal care in Oman. Early identification and management of depression and anxiety may reduce adverse outcomes for mothers and infants. Further research should investigate barriers to mental health services for pregnant women and the long-term developmental effects of antenatal depression on children.

PMID:42574733 | DOI:10.1177/17455057261476574

Am J Speech Lang Pathol. 2024 Sep 18;33(5):2438-2460. doi: 10.1044/2024_AJSLP-23-00473. Epub 2024 Aug 6.

ABSTRACT

PURPOSE: This study aimed to determine the outcomes and impact of Dynamic Temporal and Tactile Cueing (DTTC) treatment when clear vinyl masks were worn. DTTC is one of the few evidence-based treatments for children with childhood apraxia of speech (CAS). Given that DTTC relies on visual, auditory, and tactile cues, it was unknown if treatment gains would be demonstrated when masks were worn and how masking would impact the therapy experience for clinicians and caregivers.

METHOD: A sequential mixed methods design was used to study the efficacy of DTTC treatment in children with CAS when clear masks were worn. The quantitative phase used a multiple-baseline across-participants design. Four children (each 4 years of age) participated in the treatment protocol in which 24 sessions of DTTC were provided over 8 weeks while clear vinyl face masks were worn by participants and clinicians. Whole word accuracy on treated items and generalization to easy and hard untreated items were assessed during baseline, treatment, and follow-up. Semistructured interviews were conducted with clinicians and caregivers following treatment to explore the experience of masks being worn during treatment. Qualitative data were analyzed using descriptive thematic analysis.

RESULTS: Three children completed the treatment protocol. Visual and statistical analyses revealed that two participants demonstrated significant treatment effects, with one also demonstrating generalization. The remaining participant demonstrated marginal treatment gains. Qualitative findings revealed two main themes: “mask wearing was inconvenient but did not prevent therapy gains” and “in-person therapy with face masks was preferable to teletherapy.”

CONCLUSIONS: Masks did not prohibit treatment gains during DTTC therapy, with treatment effects of varying degrees shown for the three participants who completed the protocol. Together, quantitative and qualitative results indicate that mask wearing was, for most, a minor inconvenience that did not substantially interfere with the efficacy of DTTC treatment.

SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.26408854.

PMID:42574723 | DOI:10.1044/2024_AJSLP-23-00473

JMIR Med Educ. 2026 Aug 10;12:e100179. doi: 10.2196/100179.

ABSTRACT

BACKGROUND: Generative artificial intelligence (GenAI) is increasingly used to draft multiple-choice questions (MCQs) for health professions education, but much evidence concerns raw model outputs, expert ratings, or item difficulty alone. Educators edit GenAI drafts before use, and whether such items are psychometrically ready for postgraduate assessment remains unclear.

OBJECTIVE: This study aimed to compare human-edited GenAI-assisted and educator-crafted MCQs for postgraduate Family Medicine Applied Knowledge Test-level assessment, examining difficulty, discrimination, reliability, distractor functioning, and participant perceptions.

METHODS: We conducted a blinded cross-sectional, within-participant comparative psychometric evaluation in Singapore. Sixty best-of-five single-best-answer MCQs were evaluated, 30 human-edited GenAI-assisted items and 30 educator-crafted items, topic-matched across postgraduate FM domains and randomized across 2 assessment sets. Eligible participants were postgraduate doctors enrolled in FM residency or postgraduate family medicine programs, preparing for the Applied Knowledge Test, and blinded to item origin; incomplete paired responses were excluded. Outcomes included paired total scores, score correlation and agreement, Kuder-Richardson Formula 20 reliability, item difficulty index, corrected point-biserial discrimination, distractor functioning, and perceived difficulty, clarity, and relevance. Analyses used paired-sample tests, Pearson correlation, Fisher exact tests, and item-level psychometric statistics, with α=.05 and Bonferroni correction within comparison families.

RESULTS: Of 74 participants, 73 completed both item sets and were included in the analysis. The final sample comprised 36 graduate diploma in FM trainees, 5 MMed FM trainees, and 32 FM residents. Paired-sample testing showed lower scores on GenAI-assisted than educator-crafted items (mean 19.12, SD 2.83 vs mean 21.10, SD 3.42 out of 30; mean difference -1.97, 95% CI -2.72 to -1.23; P<.001; Cohen d=0.62), indicating that GenAI-assisted items were not easier. Scores were positively correlated (r=0.49, 95% CI 0.30-0.64; P<.001), but Bland-Altman analysis indicated limited agreement. Kuder-Richardson Formula 20 reliability was lower for GenAI-assisted items (0.38 vs 0.60). Mean difficulty index did not differ significantly (0.64 vs 0.70; mean difference -0.07, 95% CI -0.19 to 0.06; P=.29), and more GenAI-assisted items fell within the acceptable difficulty range (18/30, 60.0% vs 13/30, 43.3%). However, mean corrected point-biserial discrimination was lower for GenAI-assisted items (0.09 vs 0.18; mean difference -0.08, 95% CI -0.16 to -0.01; P=.04), and negative discrimination was more common (6/30, 20% vs 3/30, 10%). GenAI-assisted items also had more nonfunctioning and negatively discriminating distractors, although these differences were not statistically significant. Participant ratings of perceived difficulty, clarity, and practice relevance did not differ by origin.

CONCLUSIONS: Human-edited GenAI-assisted MCQs can achieve plausible difficulty, but difficulty and surface acceptability did not ensure assessment readiness. Using trainee response data, this study extends work on raw outputs or expert opinion. GenAI should be used as a drafting adjunct within educator-led workflows prioritizing key verification, distractor engineering, pilot testing, empirical item analysis, and repair before item-bank or summative use.

PMID:42574719 | DOI:10.2196/100179

AIDS Educ Prev. 2026 Aug;38(4):307-323. doi: 10.1521/aeap.2026.38.4.307.

ABSTRACT

To characterize the lived experience of persons living with HIV/AIDS (PLWHA) during the COVID-19 pandemic, we utilized an innovative survey to assess the experiences of 94 adult respondents receiving medical care and/or case management services from two Ryan White funded sites in New Brunswick, NJ, from May 2020 to November 2021. The Local Inventory of Needs and Knowledge-HIV (LINK-HIV) survey includes five indices and assesses overall and mental/emotional health of PLWHA. We demonstrate internal validity of the indices, describe relationships between indices and health outcomes, and characterize the health of this population. We identified specific unmet needs across the constructs of social determinants of health (SDOH) Needs, Stress due to unmet SDOH, Access to Healthcare, Stress due to COVID-19, and Person-Centered Primary Care. Respondents’ self-reported health was suboptimal (47.8% reported better overall health, 40.4% reported better mental/emotional health), and associations were seen between responses to the five indices and self-reported health outcomes.

PMID:42574704 | DOI:10.1521/aeap.2026.38.4.307

AIDS Educ Prev. 2026 Aug;38(4):284-306. doi: 10.1521/aeap.2026.38.4.284.

ABSTRACT

HIV status disclosure is an ethical obligation and, in some jurisdictions, a legal requirement that supports treatment adherence. In Eastern and Southern Africa (ESA), where intimate partner violence (IPV) is prevalent, disclosure may have harmful consequences. The objective of this scoping review was to map the relationship between IPV and HIV disclosure across health care settings in ESA. Following PRISMA-ScR guidelines, peer-reviewed English-language studies (2012-2024) were identified through EBSCOhost, PubMed, and Google Scholar using a SPICE-informed search strategy. Thirty-six quantitative, qualitative, and mixed-methods studies met the inclusion criteria and were analyzed thematically. Three themes emerged: factors influencing disclosure, including relationship dynamics and fear of violence; the positive and negative consequences of disclosure, particularly IPV; and the role of health care workers, whose limited IPV training tended to increase risk. Integrating IPV screening, safety planning, and gender-sensitive training into HIV counseling is essential to support safe, client-led disclosure.

PMID:42574702 | DOI:10.1521/aeap.2026.38.4.284

JMIR Mhealth Uhealth. 2026 Aug 10;14:e94793. doi: 10.2196/94793.

ABSTRACT

BACKGROUND: Cancer affects multiple physical, psychological, and social aspects of an individual’s life. Cancer survivors frequently report unmet needs long after diagnosis and require ongoing support. AI is increasingly embedded in patient-facing digital health technologies (DHTs) in oncology, yet its impact on different domains of patients’ and survivors’ health-related quality of life (HRQOL) remains unclear.

OBJECTIVE: This systematic review aims to (1) examine how AI has been integrated into patient-facing DHTs designed to support cancer survivors, (2) narratively synthesize the potential effects of these technologies on HRQOL and provide preliminary quantitative estimates through an exploratory meta-analysis, and (3) explore broader changes in additional patient-reported outcomes (PROs; secondary aim).

METHODS: PubMed, PsycINFO, Embase, Scopus, CINAHL, and the Cochrane Library were searched for articles published between January 2020 and August 2025. Reference lists of included articles were hand-searched for additional eligible studies. Eligible studies enrolled cancer survivors of any age and disease stage, evaluated a patient-facing DHT with AI components, and assessed HRQOL. Nonoriginal research and non-English reports were excluded. Risk of bias was assessed in all controlled studies using RoB 2 (revised Cochrane risk of bias 2) or ROBINS-I V2 (Risk of Bias in Non-Randomized Studies-of Interventions, Version 2), according to study design. Data on HRQOL and other PROs were synthesized narratively, and exploratory random-effects meta-analyses were conducted for HRQOL domains.

RESULTS: Eight reports from 7 studies from China and the United States (N=2867 participants) met the inclusion criteria, and 3 (n=292 participants) contributed to the exploratory meta-analysis. All studies included adults with various cancers at different stages and times since diagnosis. Most studies showed low risk of bias or some concerns (RoB 2), but one was evaluated as having a serious risk of bias (ROBINS-I V2). AI applications ranged from symptom monitoring to targeted education. The narrative synthesis suggested positive effects on selected HRQOL domains, particularly general health, with more pronounced effects in studies conducted in China. Exploratory meta-analyses demonstrated provisional moderate positive effects on global health (Hedges g=0.77, 95% CI 0.15-1.40) and social functioning (Hedges g=0.75, 95% CI 0.08-1.42), but no effects on physical functioning, role functioning, or emotional well-being. Other PROs indicated generally high user satisfaction and adherence, improved mental health outcomes, and reductions in physical symptoms. Only minor and mild adverse events were reported.

CONCLUSIONS: Current evidence, although limited, suggests that AI-enabled patient-facing DHTs may benefit survivors’ HRQOL and other PROs, particularly in early survivorship. However, our findings are based on small and heterogeneous studies and should therefore be interpreted with caution. Robust trials with adequate sample sizes, longer follow-up, and appropriate control conditions, including DHTs without AI components, are needed to determine the specific contribution of AI.

PMID:42574700 | DOI:10.2196/94793

JMIR Med Inform. 2026 Aug 10;14:e93680. doi: 10.2196/93680.

ABSTRACT

BACKGROUND: The medical burden caused by stroke is increasingly severe, and a small minority of high-cost patients consume the majority of medical expenditures. Therefore, revealing the formation mechanisms of this population and exploring a scientific cost-risk stratification system are crucial for improving the quality of care and achieving the optimal allocation of medical resources.

OBJECTIVE: This study aimed to construct a comorbidity network for patients with stroke using standardized front-page medical record data, extract network features that reflect complex disease interactions, and develop identification models in combination with machine learning algorithms. The study focused on building a core model integrating variables from the near-discharge stage for stratifying the risk of high hospitalization costs in patients at the near-discharge stage. In addition, an early prediction model was developed using only data available at admission.

METHODS: We conducted a retrospective study, collecting the hospital discharge data of inpatients with stroke from a tertiary hospital in Northeast China between 2021 and 2023. The data from 2021 to 2022 were used to construct a network and extract features to capture the potential relationship between diseases and high costs. Using the 2023 data partitioned into training and testing sets, we developed 5 models to identify inpatients with stroke who incurred high hospitalization costs and compared their performance when input with different features. In addition, the Shapley Additive Explanations interpretability method was adopted to explain the global and local contributions of the model features.

RESULTS: The inclusion of network features significantly improved the model’s performance, among which Extreme Gradient Boosting performed the best. The global feature importance showed that network features occupied a major proportion. The results of the Shapley Additive Explanations interaction analysis indicated potential phased changes in patient resource consumption. However, the overall performance of the early identification model constructed solely from admission data was subject to clear limitations.

CONCLUSIONS: This study developed an integrated framework combining comorbidity network analysis with machine learning, which significantly improved the accuracy of identifying inpatients with stroke at high risk of incurring excessive hospitalization costs. The core model demonstrated good performance in risk stratification during the near-discharge stage, showing potential for application in the formulation of risk management strategies and the optimization of health care resource allocation. It also laid the foundation for the subsequent development of more accurate early identification models.

PMID:42574699 | DOI:10.2196/93680

Nurs Open. 2026 Aug;13(8):e70743. doi: 10.1002/nop2.70743.

ABSTRACT

AIM/OBJECTIVE: To evaluate health science students’ learning experience and perceptions of GENIE (Gamified Asynchronous LEarNing of IPE Telehealth) and gather feedback to refine the module’s design and improve learners’ engagement.

BACKGROUND: Online telehealth education helps health science students build professional knowledge while offering flexibility in meeting clinical hour requirements. To make this learning more engaging, we developed GENIE, a gamified module based on Keller’s ARCS motivational framework. While gamification can enhance learning, how students actually interact with GENIE’s features remains unclear.

DESIGN: An exploratory mixed-methods, cross-sectional study was conducted among health science students at the University of Utah Health campus using convenience sampling. Participants were voluntarily recruited through the Interprofessional Education (IPE) program between October and December 2022.

METHODS: We conducted an exploratory mixed-methods, cross-sectional study with 30 health science students. Quantitative data were collected using the Game Experience Questionnaire (GEQ), Post-Game Experience Questionnaire (PGQ) and the Situational Motivation Scale (SIMS). Qualitative data were obtained through open-ended questions administered via REDCap.

RESULTS: Participants were primarily female nursing students (53%, n = 16). The quantitative and qualitative findings were consistent, indicating that students perceived GENIE as moderately effective in its use of gamification. GEQ results indicated low to moderate engagement, while SIMS scores reflected moderately positive motivation. PGQ findings suggest that the GENIE module was manageable and elicited neutral emotional engagement. Open-ended feedback emphasized a desire for more challenging game elements and richer interactive storytelling features.

CONCLUSIONS: This exploratory evaluation suggests that gamified online telehealth learning may support health science students’ engagement and motivation in online telehealth courses. Participants identified more challenging elements, narrative-driven interactions and interactive features as key opportunities to enhance the GENIE module. These preliminary findings can inform the future development and evaluation of gamified approaches in health science education.

IMPLICATIONS TO NURSING PRACTICE: The findings support incorporating gamification into nursing and health science education to enhance student motivation, engagement and teamwork. Gamified telehealth learning experiences may better prepare students for telehealth communication and interprofessional collaboration in future virtual care practice.

PMID:42574683 | DOI:10.1002/nop2.70743

Nurs Open. 2026 Aug;13(8):e70735. doi: 10.1002/nop2.70735.

ABSTRACT

AIM: This study aims to examine the relationship between microplastic pollution awareness and environmental literacy levels among undergraduate nursing students.

DESIGN: The study was conducted using a cross-sectional design.

METHODS: This study included 356 undergraduate nursing students selected from each academic year using proportional stratified sampling. Data were collected between March and June 2023 using a demographic form, the Microplastic Pollution Awareness Scale, and the Environmental Literacy Scale. Data analysis was performed using SPSS 23.0, with descriptive statistics, Mann-Whitney U, Kruskal-Wallis, and Spearman correlation analyses.

RESULTS: The mean microplastic pollution awareness score and environmental literacy score were 22.98 ± 4.85 and 85.21 ± 10.35, respectively. Participants who expressed willingness to receive environmental education and those who had previously heard of microplastics demonstrated higher mean scores for both microplastic pollution awareness and environmental literacy. A considerable proportion of participants reported not having previously heard of microplastics. Despite this, most nursing students (94.4%) exhibited high levels of environmental literacy. A positive correlation was found between microplastic pollution awareness and environmental literacy.

CONCLUSION: This study highlights the potential importance of environmental literacy in raising nursing students’ awareness of microplastic pollution. The current curriculum has limited content on environmental health, and it is mostly covered in a short section of the public health nursing course. These findings suggest that integrating content on environmental literacy and microplastic pollution across all stages of the nursing curriculum, as well as in in-service training, may be beneficial.

IMPLICATIONS FOR NURSING PRACTICE: Strengthening environmental literacy in nursing education may improve awareness of microplastic pollution and support nurses in promoting environmentally responsible behaviours and environmental health in clinical and community settings.

IMPACT: This study addresses the limited evidence on microplastic pollution awareness among nursing students. The findings highlight the importance of environmental literacy in shaping awareness and can inform curriculum development and educational strategies targeting future nurses.

REPORTING METHOD: This study was reported in accordance with the STROBE guidelines.

PATIENT OR PUBLIC CONTRIBUTION: No patient or public involvement.

PMID:42574677 | DOI:10.1002/nop2.70735

JMIR Mhealth Uhealth. 2026 Aug 10;14:e85197. doi: 10.2196/85197.

ABSTRACT

BACKGROUND: Advances in sensor technologies and increased adoption of wearables and smartphones by individuals have led to an abundance of patient-generated health data (PGHD). This data, when used effectively, could help to further augment the process of shared decision-making (SDM) to enable patient-centered care. However, the possible integration and usage of PGHD introduces complexities and challenges, which warrant considering both health care professional (HCP) and patient perspectives.

OBJECTIVE: Summarize the relevant works from the past 10 years that reflect the perspectives of both HCPs and patients as key stakeholders on potential barriers and enablers to the integration of PGHD for SDM. We analyzed both perspectives to surface key challenges and opportunities with integrating PGHD throughout patient journeys, as well as clinical workflows.

METHODS: Electronic searches were done using 3 databases: PubMed, ACM Digital Library, and IEEE Xplore for papers published between March 2013 and March 2023. Enablers and barriers mentioned by the stakeholders in included papers were extracted and analyzed using thematic analysis. An existing six-stage workflow model for integrating PGHD was used as a reference for deductive coding. Subsequently, considering barriers and enablers faced by both the HCPs and patients uncovered various tensions and alignments of perspectives, which could be addressed in future work and can inform concepts, designs, and development in PGHD for SDM.

RESULTS: A total of 53 publications were included in the scoping review. Six main overarching themes for barriers and enablers were identified: (1) patient-provider relationship, (2) patient characteristics, (3) organizational factors, (4) medical ethics and law, (5) data-driven workflow, and (6) design and technology. The 6-stage workflow was further expanded based on the new findings to include 4 additional stages, which include contextual considerations outside of traditional clinical environments. In addition to partially corroborating previously established barriers in the 6-stage workflow model, several new barriers and enablers were identified throughout all stages. This model helps to further align the needs of HCPs and patients beyond the clinical setting and could benefit system designers who plan to integrate PGHD for SDM.

CONCLUSIONS: This scoping review demonstrates that there are several factors to consider for effectively integrating PGHD into health-related SDM. Notably, such factors extend outside the boundaries of traditional clinical settings. Although there is agreement between HCPs and patients on certain factors, there are also tensions to be addressed. Our augmented 10-stage workflow model offers system designers an overview of the challenges and enablers to consider while designing for PGHD integration in clinical workflows and patient journeys to improve SDM.

PMID:42574670 | DOI:10.2196/85197