Medical specialty preferences in early medical school training in Canada
Bibliographic record
Abstract
Objectives: To understand what medical students consider when choosing their specialty, prior to significant clinical exposure to develop strategies to provide adequate career counseling. Methods: A cross-sectional study was performed by distributing optional questionnaires to 165 first-year medical students at the University of Ottawa in their first month of training with a sample yield of 54.5% (n=90). Descriptive statistics, analysis of variance, Spearman's rank correlation, Cronbach's alpha coefficient, Kaiser-Meyer-Olkin Measure, and exploratory factor analyses were used to analyze the anonymized results. Results: "Job satisfaction", "lifestyle following training" and, "impact on the patient" were the three highest rated considerations when choosing a specialty. Fifty-two and seventeen percent (n=24) and 57.89% (n=22) of males and females ranked non-surgical specialties as their top choice. Student confidence in their specialty preferences was moderate, meaning their preference could likely change (mean=2.40/5.00, SD=1.23). ANOVA showed no significant differences between confidence and population size (F(2,86)=0.290, p=0.75) or marital status (F(2,85)=0.354, p=0.70) in both genders combined. Five underlying factors that explained 44.32% of the total variance were identified. Five themes were identified to enhance career counseling. Conclusions: Medical students in their first month of training have already considered their specialty preferences, despite limited exposure. However, students are not fixed in their specialty preference. Our findings further support previous results but expand what students consider when choosing their specialty early in their training. Medical educators and administrators who recognize and understand the importance of these considerations may further enhance career counseling and medical education curricula.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".