Factors Related to the Choice of Family Medicine: A Reassessment and Literature Review
Bibliographic record
Abstract
BACKGROUND: Recent decreases in the number of students entering family medicine has prompted reconsideration of what is known about the factors affecting specialty choice. METHODS: Thirty-six articles on family medicine specialty choice published since 1993 were reviewed and rated for quality. RESULTS: Rural background related positively and parents' socioeconomic status relates negatively to choice of family medicine. Career intentions at entry to medical school predict specialty choice. Students who believe primary care is important, have low income expectations, and do not plan a research career are more likely to choose family medicine. The school characteristic related to choice of family medicine is public ownership. Large programs to increase numbers entering primary care seem effective. Required family medicine time in clinical years is related to higher numbers selecting family medicine. Faculty role models serve both as positive and negative influences. Students rejecting family medicine are concerned about prestige, low income, and breadth of knowledge required. Students planning on a career in a disadvantaged or rural area are more likely to enter family medicine. CONCLUSIONS: Multiple factors are consistently shown to be related to the choice of the specialty of family medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".