Career decision making in undergraduate medical education
Bibliographic record
Abstract
BACKGROUND: It is unclear how medical students prioritize different factors when selecting a specialty. With rising under and unemployment rates a novel approach to career counselling is becoming increasingly important. A better understanding of specialty selection could lead to improved career satisfaction amongst graduates while also meeting the health care needs of Canadians. METHODS: Medical students from the University of Toronto participated in a two-phase study looking at factors impacting specialty selection. Phase I consisted of focus groups, conducted independently for each year, and Phase II was a 21-question electronic survey sent to all students. RESULTS: Twenty-one students participated in the focus group phase and 95 in the survey phase. Primary themes related to career selection identified in Phase I in order of frequency included personal life factors (36), professional life factors (36), passion/interest (20), changing interests (19) and hidden curriculum (15). The survey phase had similar results with passion (83), lifestyle (79), flexibility (75), employment opportunities (60) and family (50) being ranked as the factors most important in specialty selection. CONCLUSION: Personal factors, professional factors and passion/interest may be key themes for medical students when deciding which specialty to pursue. Targeting career counselling around these areas may be important.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".