Primary care specialty career choice among Canadian medical students: Understanding the factors that influence their decisions.
Bibliographic record
Abstract
OBJECTIVE: To identify which factors influence medical students' decision to choose a career in family medicine and pediatrics, and which factors influence their decision to choose careers in non-front-line specialties. DESIGN: Survey that was created based on a comprehensive literature review to determine which factors are considered important when choosing practice specialty. SETTING: Ontario medical school. PARTICIPANTS: An open cohort of medical students in the graduating classes of 2008 to 2011 (inclusive). MAIN OUTCOME MEASURES: The main factors that influenced participants' decision to choose a career in primary care or pediatrics, and the main factors that influenced participants' decision to choose a career in a non-front-line specialty. RESULTS: < .001). CONCLUSION: In this study, 8 factors were found to positively influence medical students' career choice in family medicine and pediatrics, and 6 factors influenced the decision to choose a career in a non-front-line specialty. Medical students can be encouraged to explore a career in family medicine or pediatrics by addressing misinformation, by encouraging realistic expectations of career outcomes in the various specialties, and by demonstrating the capacity of primary care fields to incorporate specific motivating factors.
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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 | 0.002 | 0.011 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 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".