Why medical students switch careers: changing course during the preclinical years of medical school.
Bibliographic record
Abstract
OBJECTIVE: To determine why students switch their career choices during the preclinical years of medical school. DESIGN: Two questionnaires were administered: the first at the beginning of medical school and the second about 3 years later just before students entered clinical clerkship. SETTING: University of British Columbia, University of Alberta, University of Toronto, University of Ottawa, Queen's University, University of Western Ontario, University of Calgary, and McMaster University. PARTICIPANTS: Entering cohorts from 10 medical school classes at 8 Canadian medical schools. MAIN OUTCOME MEASURES: Proportion of students who switched career choices and factors that influenced students to switch. RESULTS: Among the 845 eligible respondents to the second survey, 19.6% (166 students) had switched between categories of family medicine and specialties, with a net increase of 1.2% (10 students) to family medicine. Most students who switched career choices had already considered their new careers as options when they entered medical school. Seven factors influenced switching career choices; 6 of these (medical lifestyle, encouragement, positive clinical exposure, economics or politics, competence or skills, and ease of residency entry) had significantly different effects on students who switched to family medicine than on students who switched out of family medicine. The seventh factor was discouragement by a physician. CONCLUSION: Seven factors appear to affect students who switch careers. Two of these factors, economics or politics and ease of residency entry, have not been previously described in the literature. This study provides specific information on why students change their minds about careers before they get to the clinical years of medical training.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".