Which Students Will Choose a Career in Psychiatry?
Bibliographic record
Abstract
OBJECTIVE: In Canada, availability of and access to mental health professionals is limited. Only 6.6% of practising physicians are psychiatrists, a situation unlikely to improve in the foreseeable future. Identifying student characteristics present at medical school entry that predict a subsequent psychiatry residency choice could allow targeted recruiting or support to students early on in their careers, in turn creating a supply of psychiatry-oriented residency applicants. METHOD: Between 2002 and 2004, data were collected from students in 15 Canadian medical school classes within 2 weeks of commencement of their medical studies. Surveys included questions on career preferences, attitudes, and demographics. Students were followed through to graduation and entry data linked anonymously with residency choice data. Logistic regression was used to identify early predictors of a psychiatry residency choice. RESULTS: Students (n = 1502) (77.4% of those eligible) contributed to the final analysis, with 5.3% naming psychiatry as their preferred residency career. When stated career interest in psychiatry at medical school entry was not included in a regression model, an exit career choice in psychiatry was predicted by a student's desire for prestige, lesser interest in medical compared with social problems, low hospital orientation, and not volunteering in sports. When an entry career interest in psychiatry was included in the model, this variable became the only predictor of an exit career choice in psychiatry. CONCLUSION: While experience and attitudes at medical school entry can predict whether students will chose a psychiatry career, the strongest predictor is an early career interest in psychiatry.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".