Attitudes and factors contributing to attrition in Canadian surgical specialty residency programs
Bibliographic record
Abstract
Background: We recently studied attrition in Canadian general surgical programs; however, there are no data on whether residents enrolled in other surgical residencies harbour the same intents as their general surgical peers. We sought to determine how many residents in surgical disciplines in Canada consider leaving their programs and why. Methods: An anonymous survey was administered to all residents in 9 surgical disciplines in Canada. Significance of association was determined using the Pearson χ2 test. The Canadian Post-MD Education Registry (CAPER) website was used to calculate the response rate. Results: We received 523 responses (27.6% response rate). Of these respondents, 140 (26.8%) were either “somewhat” or “seriously” considering leaving their program. Residents wanting to pursue additional fellowship training and those aspiring to an academic career were significantly less likely to be considering changing specialties (p = 0.003 and p = 0.005, respectively). Poor work–life balance and fear of unemployment/underemployment were the top reasons why residents would change specialty (55.5% and 40.8%, respectively), although the reasons cited were not significantly different between those considering changing and those who were not (p = 0.64). Residents who were considering changing programs were significantly less likely to enjoy their work and more likely to cite having already invested too much time to change as a reason for continuing (p < 0.001). Conclusion: More than one-quarter of residents in surgical training programs in Canada harbour desires to abandon their surgical careers, primarily because of unsatisfactory work–life balance and limited employment prospects. Efforts to educate prospective residents about the reality of the surgical lifestyle and to optimize employment prospects may improve completion rates.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".