Shaping the future of primary healthcare: Factors influencing medical students’ preference for family medicine specialty in Qatar
Bibliographic record
Abstract
BACKGROUND: As countries strive to strengthen primary healthcare systems, understanding medical students' specialty preferences, particularly for Family Medicine, becomes crucial. Objective of present study was to determine factors that influence medical students' preferences for Family Medicine in Qatar. MATERIALS AND METHODS: A study was conducted among medical students at one public university in Qatar from March 1, 2023, to March 15, 2023. Data collected using a structured, validated questionnaire; students participated in the study by filling an online questionnaire. Information sought included sociodemographic characteristics, medical specialty preferences, and the impact of 31 factors on these choices such as interest in dealing with diverse patient issues, and the influence of physician role models among others. Statistical analysis performed using SPSS version 26.0 Descriptive statistics, including frequencies and percentages, were used to summarize participant demographics and medical specialty preferences. Mann-Whitney U-test tested the difference in preference for family medicine specialty by various students' characteristics. Logistic regression analysis performed to identify factors related to preferring Family Medicine. RESULTS: A total of 262 students completed the survey with age ranging between 17-24 years (Mean=20, SD=1.7 years); majority (68.3%) were females and 54.4% were non-Qatari. About 16% students reported having a first degree relative or a family friend with family medicine specialty. Compared to surgery and internal medicine, a fewer students selected family medicine as their preferred specialty. Surgery was ranked as the top career choice (53.4%) followed by internal medicine (36.5%) and pediatrics (31.7%). Presence of a personal connection to a field seemed to have a significant impact on student preference. Being in the clerkship phase of medical education (adjusted odds ratio [AOR]=2.18, 95% CI: 1.12-4.23, P = 0.022) and possessing a personal connection to Family Medicine (AOR= 3.46, 95% CI: 1.65-7.29, P = 0.001) significantly predicted the selection of Family Medicine between students' top two choices. CONCLUSION: Clinical exposure and personal connections influence students' preference for family medicine specialty. Promoting these elements through targeted educational and mentorship programs may increase students' interest in Family Medicine and help to address the gap between student preferences and the greater need for primary care providers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".