Why would I choose a career in family medicine?: Reflections of medical students at 3 universities.
Bibliographic record
Abstract
OBJECTIVE: To describe the factors that medical students report influence them to pursue careers in family medicine. DESIGN: Qualitative study using focus groups and interviews and the results of surveys conducted at 3 different points in medical education. SETTING: Three medical schools in western Canada: the University of British Columbia in Vancouver, the University of Calgary in Alberta, and the University of Alberta in Edmonton. PARTICIPANTS: A total of 33 medical students. METHOD: Students were surveyed during the first 2 weeks of their programs, at the end of their preclinical training, and again at the end of their clinical training on their interest in family medicine or other specialty areas. Focus groups and interviews were conducted to explore the reasons students gave for an emerging or final interest in family medicine as a career choice. A small cohort of students who stayed with another specialty choice or switched to another specialty from family medicine were also interviewed. Thematic content analysis was carried out. MAIN FINDINGS: Students identified several important influences that were subdivided into pre-medical school, medical school, postgraduate training, and life-in-medicine influences. Many positive and negative aspects of family medicine were reported during the preclinical period. Clinical exposure was critical for demonstrating the positive aspects of family medicine. Postgraduate training, future practice, and nonpractice life considerations also influenced students' career choices. CONCLUSION: This study provides a qualitative understanding of why students choose careers in family medicine. Medical schools should offer high-quality family medicine clinical experiences, consider the potentially positive influence of rural settings, and provide early and accurate information on family medicine training and career opportunities. These interventions might help students make more informed career decisions and increase the likelihood that they will consider careers in family medicine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".