Category: Nevin Manimala

Fam Med. 2025 Apr 9. doi: 10.22454/FamMed.2025.256581. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVES: The choice of specialty by medical students is pivotal for their careers and the health care system. The shortage of trained medical providers makes this choice particularly salient in Africa. Understanding the motivations and preferences of African medical students can inform interventions to improve the distribution and retention of doctors across specialties and regions. This study aims to explore the factors influencing specialty selection among medical students across Africa using a cross-sectional, mixed-methods approach.

METHODS: A survey conducted from June to October 2023 included 1,044 students from 152 medical schools. Descriptive statistics summarized sample characteristics, and linear regression models identified predictors of primary care selection. Thematic analysis was performed on qualitative data.

RESULTS: Students interested in primary care were, on average, older and reported higher anxiety levels compared to their counterparts. Key factors influencing specialty choice across all students included personal interest, scope of practice, and intellectual stimulation, with prestige being least important for those choosing primary care. Additionally, the importance of mentorship was lower among primary care aspirants. Country-specific analysis revealed that students from Benin, Botswana, Ivory Coast, Senegal, and Sierra Leone were more likely to choose primary care.

CONCLUSIONS: This study provides an overview of the motivations behind specialty choice among African medical students, highlighting the need for tailored interventions to address regional health care needs. Understanding these preferences can help in designing strategies to enhance the distribution and retention of medical professionals in various specialties, ultimately improving health care outcomes.

PMID:40267491 | DOI:10.22454/FamMed.2025.256581

Fam Med. 2025 Apr 4. doi: 10.22454/FamMed.2025.344685. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVES: The University of California, Los Angeles (UCLA) International Medical Graduate (IMG) program addresses the need for more bilingual and bicultural Latino family physicians in California where Latinos are the largest racial/ethnic minority group and a large percentage of the population speaks Spanish. The objective of this descriptive study was to assess family medicine residency match, board certification, and initial practice location outcomes of the program graduates.

METHODS: We conducted a cross-sectional study of program graduates (N=204) from 2007 to 2024. Data were abstracted from program administrative files and the California Medical Board. Primary outcomes were match rate into California family medicine residency programs, completion of a residency, board certification, and initial training practice location. We computed descriptive statistics for participant characteristics and outcomes.

RESULTS: A total of 177/204 (87%) participants completed the UCLA IMG program and entered the match. The country with the most graduates was Mexico followed by Cuba. All graduates, 177/177 (100.0%), that applied and entered the National Resident Matching Program matched in a family medicine residency program. A total of 172 (97%) matched in California programs and 5 (2.8%) matched out of state. Family medicine board certification was verified for 152/159 (95.6%) of those eligible. Few completed a fellowship.

CONCLUSIONS: The UCLA IMG program was effective at preparing program graduates that were fluent in Spanish and bicultural to match in a California family medicine residency program and subsequently practice family medicine in underserved areas. Future studies will examine long-term practice outcomes, predictors of success, and participant perspectives on the program.

PMID:40267486 | DOI:10.22454/FamMed.2025.344685

Obstet Gynecol. 2023 Dec 1;142(6):1477-1485. doi: 10.1097/AOG.0000000000005421. Epub 2023 Oct 26.

ABSTRACT

OBJECTIVE: To compare contraceptive provision to women with and without intellectual and developmental disabilities enrolled in North Carolina Medicaid.

METHODS: Our retrospective cohort study used 2019 North Carolina Medicaid claims to identify women aged 15-44 years with and without intellectual and developmental disabilities at risk for pregnancy who were continuously enrolled during 2019 or had Family Planning Medicaid with at least one claim. We calculated the proportion in each cohort who received 1) most or moderately effective contraception, 2) long-acting reversible contraception, 3) short-acting contraception, and 4) individual methods. We classified contraceptive receipt by procedure type and disaggregated across sociodemographic characteristics. Adjusting for age, race, ethnicity, and urban or rural setting, we constructed logistic regression models to estimate most or moderately effective contraceptive provision odds by intellectual and developmental disability status and by level or type of intellectual and developmental disability. We performed subanalyses to estimate co-occurrence of provision and menstrual disorders.

RESULTS: Among 9,508 women with intellectual and developmental disabilities and 299,978 without, a significantly smaller proportion with intellectual and developmental disabilities received most or moderately effective contraception (30.1% vs 36.3%, P <.001). With the exception of injectable contraception, this trend was consistent across all measures and remained statistically significant after controlling for race, ethnicity, age, and urban or rural status (adjusted odds ratio 0.75, 95% CI 0.72-0.79; P <.001). Among those who received most or moderately effective contraception, a significantly greater proportion of women with intellectual and developmental disabilities had co-occurring menstrual disorders (31.3% vs 24.3%, P <.001).

CONCLUSION: These findings suggest disparities in contraceptive provision and potential differences in clinical indication by intellectual and developmental disability status. Future studies should investigate reasons for and barriers to contraceptive use among women with intellectual and developmental disabilities.

PMID:40267481 | DOI:10.1097/AOG.0000000000005421

JMIR Med Inform. 2025 Apr 23;13:e57530. doi: 10.2196/57530.

ABSTRACT

BACKGROUND: Consuming high amounts of foods or beverages with high levels of saturated fats, salt, or sugar (HFSS) can be harmful for health. Many snacks fall into this category (HFSS snacks). However, the palatability of these snacks means that people can sometimes struggle to reduce their intake. Machine learning algorithms could help in predicting the likely occurrence of HFSS snacking so that just-in-time adaptive interventions can be deployed. However, HFSS snacking data have certain characteristics, such as sparseness and incompleteness, which make snacking prediction a challenge for machine learning approaches. Previous attempts have employed several potential predictor variables and have achieved considerable success. Nevertheless, collecting information from several dimensions requires several potentially burdensome user questionnaires, and thus, this approach may be less acceptable for the general public.

OBJECTIVE: Our aim was to consider the capacity of standard (unmodified in any way; to tailor to the specific learning problem) machine learning algorithms to predict HFSS snacking based on the following minimal data that can be collected in a mostly automated way: day of the week, time of the day (divided into time bins), and location (divided into work, home, and other).

METHODS: A total of 111 participants in the United Kingdom were asked to record HFSS snacking occurrences and the location category over a period of 28 days, and this was considered the UK dataset. Data collection was facilitated by a purpose-specific app (Snack Tracker). Additionally, a similar dataset from the Netherlands was used (Dutch dataset). Both datasets were analyzed using machine learning methods, including random forest regressor, Extreme Gradient Boosting regressor, feed forward neural network, and long short-term memory. We additionally employed 2 baseline statistical models for prediction. In all cases, the prediction problem was the time to the next HFSS snack from the current one, and the evaluation metric was the mean absolute error.

RESULTS: The ability of machine learning methods to predict the time of the next HFSS snack was assessed. The quality of the prediction depended on the dataset, temporal resolution, and machine learning algorithm employed. In some cases, predictions were accurate to as low as 17 minutes on average. In general, machine learning methods outperformed the baseline models, but no machine learning method was clearly better than the others. Feed forward neural network showed a very marginal advantage.

CONCLUSIONS: The prediction of HFSS snacking using sparse data is possible with reasonable accuracy. Our findings offer a foundation for further exploring how machine learning methods can be used in health psychology and provide directions for further research.

PMID:40267467 | DOI:10.2196/57530

JMIR Mhealth Uhealth. 2025 Apr 23;13:e56318. doi: 10.2196/56318.

ABSTRACT

BACKGROUND: Chronic obstructive pulmonary disease (COPD) negatively impacts clinical health outcomes, resulting in frequent exacerbations, increased hospitalizations, reduced physical activity, deteriorated quality of life, and diminished self-efficacy. Previous studies demonstrated that a self-management program tailored for adults with COPD improves self-management decisions, resulting in a positive effect on clinical health outcomes. Limitations of these studies include issues regarding heterogeneity among interventions used, patient population characteristics, outcome measures, and longitudinal studies. Limited studies focused on the use of a comprehensive self-management program using a smartphone app for adults with COPD over 12 months.

OBJECTIVE: This study aimed to explore the effectiveness of a smartphone app self-management program and monthly phone calls compared with standard respiratory outpatient care on clinical health outcomes in adults with COPD.

METHODS: This was a 3-arm parallel pilot randomized controlled trial (RCT) that included 92 participants. Participants were randomized into intervention arm 1, which included a self-management smartphone app and monthly phone calls (n=31); intervention arm 2, which included a self-management smartphone app (n=31); and arm 3, which was standard respiratory outpatient care (n=30). All arms received standard respiratory outpatient care. The primary outcome was a binary indicator equal to 1 if participants reported attendance to a general practitioner (GP) and or a hospital setting as a result of an exacerbation and 0 otherwise. This indicator was recorded at 6 months and 12 months from the baseline. Secondary outcomes included engagement, breathlessness, physical activity, health-related quality of life, and self-efficacy.

RESULTS: There was a statistically significant difference (P=.03), indicating fewer exacerbations in the intervention arm 2 compared with the control arm at 6 months in the hospital setting. The intervention arms had a statistically significant difference indicating a lower risk of developing an exacerbation at 6 months in both the GP (P=.01) and hospital setting (P=.006) compared to the control arm. Furthermore, intervention arm 1 demonstrated a statistically significant difference in exercise capacity at 6 and 12 months (P=.02 and P=.03). The intervention arm 2 illustrated a statistically significant difference in step count (P=.009) compared to the control arm. The majority of participants (60%, 33/55) used the app over the 12-month period.

CONCLUSIONS: This study demonstrated that a smartphone app self-management program had a positive effect on clinical health outcomes for participants with COPD in comparison to standard respiratory outpatient care. This study illustrated benefits such as reduced exacerbations resulting in fewer hospitalizations, improved exercise capacity, and physical activity among the intervention arms. This was a single-center study, which was limited in power to demonstrate significant effects on all measured outcomes but paves the way for a larger, fully powered multicenter trial exploring the effect of a smartphone app self-management program on clinical health outcomes in adults with COPD.

TRIAL REGISTRATION: ClinicalTrials.gov NCT05061810; https://clinicaltrials.gov/study/NCT05061810.

PMID:40267465 | DOI:10.2196/56318

JMIR Med Educ. 2025 Apr 23;11:e45912. doi: 10.2196/45912.

ABSTRACT

BACKGROUND: Nursing students’ motivation in clinical learning is very important not only for their academic and professional achievement but also for making timely, informed, and appropriate decisions in providing quality and cost-effective care to people. However, the increased number of students and the scarcity of medical supplies, equipment, and patients, just to mention a few, have posed a challenge to educators in identifying and navigating the best approaches to motivate nursing students to learn during their clinical placements.

OBJECTIVE: This study primarily used descriptive and analytical methods to examine undergraduate nursing students’ desire for clinical learning both before and after participating in the program.

METHODS: An uncontrolled longitudinal quasi-experimental study in a quantitative research approach was conducted from February to March 2021 among 589 undergraduate nursing students in Tanzania. Following a baseline evaluation, nursing students were enrolled in an interactive web-based clinical practice monitoring system by their program, institution, names, registration numbers, and emails via unique codes created by the lead investigator and trainers. The system recorded and generated feedback on attendance, clinical placement unit, selected or performed clinical nursing procedures, and in-between and end-of-shift feedback. The linear regression was used to assess the effect of the intervention (interactive web-based clinical practice monitoring system) controlled for other correlated factors on motivation in clinical learning (outcome) among nursing students. Nursing students’ sociodemographic characteristics and levels of motivation in clinical learning were analyzed descriptively while a 2-tailed paired sample t test established a comparative mean difference in motivation in clinical learning between the pretest and the posttest. The association between variables was determined using regression analysis set at a 95% CI and 5% statistical significance.

RESULTS: The mean age of study participants (N=589) was 23 (SD 2.69) years of which 383 (65.0%) were male. The estimated effect (β) of a 3-week intervention to improve nursing students’ motivation in clinical learning was 3.041 (P=.03, 95% CI 1.022-7.732) when controlled for other co-related factors. The mean score for motivation in clinical learning increased significantly from the baseline (mean 9.31, SD 2.315) to the postintervention (mean 20.87, SD 5.504), and this improvement presented a large effect size of 2.743 (P<.001, 95% CI 1.011-4.107).

CONCLUSIONS: Findings suggest that an interactive web-based clinical practice monitoring system is viable and has the potential to improve undergraduate nursing students’ motivation for clinical learning. One alternative clinical pedagogy that educators in nursing education can use to facilitate clinical learning activities and develop motivated undergraduate nursing students is the integration of such technology throughout nursing curricula.

PMID:40267464 | DOI:10.2196/45912

J Strength Cond Res. 2025 Apr 23. doi: 10.1519/JSC.0000000000005081. Online ahead of print.

ABSTRACT

Edwards, PK, Blackah, N, Ebert, JR, and Chapman, D. Sex- and position-specific countermovement jump outcome and phase characteristics in Australian rules football players. J Strength Cond Res XX(X): 000-000, 2024-This study was designed to examine positional and sex differences in countermovement jump (CMJ) force-time characteristics among subelite male (n = 111) and female (n = 71) Australian football (AF) players and establish normative data for key performance output metrics. A total of 182 male and female players performed 3 maximal effort CMJs on dual force plates. Differences in variables including jump height, peak power, modified reactive strength index (RSIMOD), and other force-time variables were compared using 1-way ANOVA and Cohen’s d effect sizes. Statistical significance was set at p ≤ 0.05. Male players demonstrated significantly greater jump height (36.0 ± 5.2 cm vs. 25.9 ± 4.2 cm; p < 0.001) and RSIMOD (0.52 ± 0.11 vs. 0.39 ± 0.09; p < 0.001) compared with female players, with large effect sizes (d = 1.18 and 2.08, respectively). Positional differences were observed in both sexes. Male midfielders exhibited a significantly shorter time to takeoff compared with backs (mean difference, -67.1 ms; p = 0.008). Female midfielders, compared with backs, also recorded significantly shorter time to takeoff (mean difference, -79.0 ms; p = 0.014) and higher RSIMOD values (mean difference, 0.05; p = 0.048). No significant differences were found in jump height or peak power between playing positions for either sex. These findings highlight different performance profiles between sexes and playing positions in AF, offering practitioners normative benchmarks to guide return-to-play decisions and optimize player physical development.

PMID:40267461 | DOI:10.1519/JSC.0000000000005081

JMIR Med Inform. 2025 Apr 23;13:e63709. doi: 10.2196/63709.

ABSTRACT

BACKGROUND: Critically ill patients in intensive care units (ICUs) require continuous monitoring, generating vast amounts of data. Clinical decision support systems (CDSS) leveraging artificial intelligence (AI) technologies have shown promise in improving diagnostic, prognostic, and therapeutic decision-making. However, these models are rarely implemented in clinical practice.

OBJECTIVE: The aim of this study was to survey ICU physicians to understand their expectations, opinions, and level of knowledge regarding a proposed AI-based CDSS for continuous renal replacement therapy (CRRT) weaning, a clinical decision-making process that is still complex and lacking in guidelines. This will be used to guide the development of an AI-based CDSS on which our team is working to ensure user-centered design and successful integration into clinical practice.

METHODS: A prospective cross-sectional survey of French-speaking physicians with clinical activity in intensive care was conducted between December 2023 and April 2024. The questionnaire consisted of 20 questions structured around 4 axes: overview of the problem and current practices concerning weaning from CRRT, opinion on AI-based CDSS, implementation in daily clinical practice, real-life operation and willingness to adopt the CDSS in everyday practice. Statistical analyses included Wilcoxon rank sum tests for quantitative variables and χ2 or Fisher exact tests for qualitative variables, with multivariate analyses performed using ordinal logistic regression.

RESULTS: A total of 171 complete responses were received. Physicians expressed an interest in a CDSS for CRRT weaning, with 70.2% (120/171) viewing AI-based CDSS favorably. Opinions were split regarding the difficulty of the weaning decision itself, with 46.2% (79/171) disagreeing that it is challenging, while 31.6% (54/171) agreed. However, 66.1% (113/171) of respondents supported the value of an AI-based CDSS to assist them in this decision, with younger physicians showing stronger support (81.8%, 27/33 vs 62.3%; 86/138; P=.01). Most respondents (163/171, 95.3%) emphasized the importance of understanding the criteria used by the model to make its predictions.

CONCLUSIONS: Our findings highlight an optimistic attitude among ICU physicians toward AI-based CDSS for CRRT weaning, emphasizing the need for transparency, integration into existing workflows, and alignment with clinicians’ decision-making processes. Actionable recommendations include incorporating key variables such as urine output and biological parameters, defining probability thresholds for recommendations and ensuring model transparency to facilitate the successful adoption and integration into clinical practice. The methodology of this survey may help the development of further predevelopment studies accompanying AI-based CDSS projects.

PMID:40267422 | DOI:10.2196/63709

J Strength Cond Res. 2025 Apr 23. doi: 10.1519/JSC.0000000000005121. Online ahead of print.

ABSTRACT

Meechan, D, McErlain-Naylor, SA, Phua, Juan Peng, and Comfort, P. The effect of load on subphase analysis during the hang pull. J Strength Cond Res XX(X): 000-000, 2025-The effect of load on temporally aligned time-series data has yet to be investigated during weightlifting derivatives. Such data may provide greater insight regarding any differences in stimulus between relative loads during each phase. This study compared the effect of load on the force-time and velocity-time curves during the hang pull (HP). Twenty-seven males performed the HP at relative loads of 40, 60, 80, 100, 120, and 140% one repetition maximum (1RM) power clean (PC). A force plate measured the vertical ground reaction force, which calculated the barbell-lifter system velocity. Time-series were time-normalized to 101 data points (0-100% of the movement duration) via piecewise linear length normalization of the individual phases (unweighting, braking, propulsion) and assessed via statistical parametric mapping. Relative loads of 40% 1RM PC maximized propulsion velocity, whereas 140% 1RM maximized force. Statistical parametric mapping analysis showed greater force at 140% 1RM PC throughout 55-100% of total movement duration compared with all loads, with greater propulsion velocity at lighter loads during the propulsion phase (79-100% of the movement) for all loads, with greater negative velocity at 140% 1RM PC compared with 60 and 100% 1RM PC during late unweighting/early braking phase (32-54% of the movement). Braking, propulsion, and total absolute durations increased with load. It may be appropriate to prescribe the HP during a maximal strength and strength-speed mesocycle given the ability to use supramaximal loads.

PMID:40267416 | DOI:10.1519/JSC.0000000000005121

Hum Factors. 2025 Apr 23:187208251335149. doi: 10.1177/00187208251335149. Online ahead of print.

ABSTRACT

ObjectiveThis study investigated how user characteristics and driving context influence auditory experiences (AX) in electric vehicles (EVs), identifying distinct user types and their specific auditory needs and evaluation.BackgroundElectric vehicles (EVs) present unique opportunities for designing auditory experiences (AX) due to their quiet operation characteristics and acoustic vehicle alert systems (AVAS).MethodForty participants conducted real-driving experiments with an EV, experiencing sounds at low, medium, and high speeds. We applied systematic analysis combining topic modeling (BERTopic) and qualitative coding of think-aloud interviews and statistical analysis of questionnaire responses.ResultsFour user types were segmented by attitude (Dynamic vs. Conservative) and car type (EV vs. ICV owners). Text analysis revealed varying frequencies of concerns across user types regarding driving contexts, functional aspects, and affective aspects of AX. Statistical analysis showed significant differences among user types in sporty preferences and perceptions of affective properties (Sporty, Stylish, Comfort, and Calm). Driving contexts significantly influenced perceived Stylish and Calm characteristics.ConclusionThis study provides empirical evidence and design implications for customized AX in EVs design based on user characteristics and driving contexts.ApplicationThe findings can guide the development of personalized AX systems in EVs, enhancing both user satisfaction and safety through context-aware and user-centered design approaches.

PMID:40267415 | DOI:10.1177/00187208251335149