Trainees’ views of physician workforce policy in Quebec and their impact on career intentions
Bibliographic record
Abstract
Background: The physician workforce in Quebec is regulated by a government-controlled plan. Many specialty trainees expressed concerns about securing a position. Our objective was to analyze physicians’ employment issues in Quebec and their impact on residents’ training in specialty programs. Methods: We distributed a web-based self-administrated survey to all Quebec residents training in specialty programs to capture data about residents’ ability to find employment, career plans and perceptions regarding the workforce policy. Three groups were considered: graduates, non-graduating senior residents, and junior residents. Results: The overall response rate was 41.5% (985/2372). 47.3% of graduates did not have a position two months before finishing their training. Among residents without a position, 27.1% of graduates intend to leave Quebec, and 19.6% to complete a fellowship to postpone their start in practice. Overall, 77.9% of respondents believed there are not enough job opportunities for the number of trainees. Conclusion: Quebec specialty residents experience significant difficulties obtaining a position in the province and perceive that there are not enough job opportunities, which impacts their career plans and could drive them to complete a fellowship or plan to practice outside the province. Trainees' experience in finding employment needs to be considered in planning the physician workforce.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".