Determinants of choosing a career in family medicine
Bibliographic record
Abstract
BACKGROUND: Student choice is an important determinant of the distribution of specialties of practising physicians in many countries. Understanding characteristics at entry into medical school that are associated with the choice of residency in family medicine can assist medical schools in admitting an appropriate mix of students to serve the health care needs of their regions. METHODS: From 2002 to 2004, we collected data from students in 15 classes at 8 of 16 Canadian medical schools at entry. Surveys included questions on career choice, attitudes to practice and socio-demographic characteristics. We followed students prospectively with these data linked to their residency choice. We used multiple logistic regression analysis to identify entry characteristics that predicted a student's ultimate career choice in family medicine. RESULTS: Of 1941 eligible students in the participating classes, 1542 (79.4%) contributed data to the final analyses. The following 11 entry variables predicted whether a student named family medicine as his or her top residency choice: being older, being engaged or in a long-term relationship, not having parents with postgraduate university education nor having family or close friends practicing medicine, having undertaken voluntary work in a developing nation, not volunteering with elderly people, desire for varied scope of practice, a societal orientation, a lower interest in research, desire for short postgraduate training, and lower preference for medical versus social problems. INTERPRETATION: Demographic and attitudinal characteristics at entry into medical school predicted whether students chose a career 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".