Attractiveness of family medicine for medical students
Bibliographic record
Abstract
Objective To examine the association between students’ personal characteristics, backgrounds, and medical schools and their intention to enter a family medicine (FM) specialty. Design Descriptive study using data from the 2007 National Physician Survey. Setting Canada. Participants Clinical (n = 1109) and preclinical (n = 829) medical student respondents to the 2007 National Physician Survey. Main outcome measures The main variable was hoping to enter an FM specialty, and 40 independent variables were included in regression and classification-tree models. Results Fewer than 1 medical student in 3 (30.2% at the preclinical level and 31.4% at the clinical level) hoped to enter into an FM career. Those who did were more likely to be female, were slightly older, were more frequently married or living with partners, were typically born in Canada, and were more likely to have previous exposure to non-urban environments. The most important predictor for both populations was the debt related to medical studies, which acted in the opposite direction of whether or not students were interested in research. Students interested in research were attracted by specialties with high earning potential, while those not interested in research looked for short residency programs, such as FM, so they could begin to pay off debt sooner. Therefore, the interest in research appears to be inversely related to the choice of FM. Conclusion Less than one-third of medical students were looking for residencies in FM in Canada. This is far below the goals of 45% set at the national level and 50% set by some provinces like Quebec. Debt and interest in research have strong influences on the choice of residency by medical students.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.013 | 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".