The happy docs study: a Canadian Association of Internes and Residents well-being survey examining resident physician health and satisfaction within and outside of residency training in Canada
Bibliographic record
Abstract
BACKGROUND: Few Canadian studies have examined stress in residency and none have included a large sample of resident physicians. Previous studies have also not examined well-being resources nor found significant concerns with perceived stress levels in residency. The goal of "The Happy Docs Study" was to increase knowledge of current stressors affecting the health of residents and to gather information regarding the well-being resources available to them. FINDINGS: A questionnaire was distributed to all residents attending all medical schools in Canada outside of Quebec through the Canadian Association of Internes and Residents (CAIR) during the 2004-2005 academic years.In total 1999 resident physicians responded to the survey (35%, N = 5784 residents). One third of residents reported their life as "quite a bit" to "extremely" stressful (33%, N = 656). Time pressure was the most significant factor associated with stress (49%, N = 978). Intimidation and harassment was experienced by more than half of all residents (52%, N = 1050) with training status (30%, N = 599) and gender (18%, N = 364) being the main perceived sources. Eighteen percent of residents (N = 356) reported their mental health as either "fair" or "poor". The top two resources that residents wished to have available were career counseling (39%, N = 777) and financial counseling (37%, N = 741). CONCLUSION: Although many Canadian resident physicians have a positive outlook on their well-being, residents experience significant stressors during their training and a significant portion are at risk for emotional and mental health problems. This study can serve as a basis for future research, advocacy and resource application for overall improvements to well-being during residency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".