Stress, burnout, and strategies for reducing them: what's the situation among Canadian family physicians?
Bibliographic record
Abstract
OBJECTIVE: To ascertain Canadian family physicians' levels of stress and burnout and the strategies they use to reduce these problems. DESIGN: Census survey. SETTING: Kitchener-Waterloo, an urban area with a population of approximately 300 000 in southwestern Ontario. PARTICIPANTS: Family physicians. MAIN OUTCOME MEASURES: Scores on the Family Physician Stress Inventory, scores on strategies to reduce personal stress, scores on strategies to reduce stress on the job, and scores on the Maslach Burnout Inventory. RESULTS: Participation rate was 77.8% (123 of 158 surveys returned). About 42.5% of participants had high stress levels. Burnout was defined by 3 components: emotional exhaustion, depersonalization (going through the day like an "automaton"), and perceived lack of personal accomplishment. Many respondents scored high on the burnout inventory, and almost half had high levels of emotional exhaustion and depersonalization (47.9% and 46.3%, respectively). No demographic factors were associated with high scores on these components. Use of strategies to reduce personal and occupational stress was associated with lower levels of burnout. Scores on the Family Physician Stress Inventory correlated highly with scores on the Maslach Burnout Inventory. CONCLUSION: Regardless of demographic factors, family physicians are at risk of having high levels of stress and burnout. Classic burnout is related to stress brought on by factors such as too much paperwork, long waits for specialists and tests, feeling undervalued, feeling unsupported, and having to abide by rules and regulations. Common strategies for reducing personal stress included eating nutritiously and spending time with family and friends. Common strategies for reducing stress on the job included valuing relationships with patients and participating in continuing medical education. Stress and burnout are related to the desire to give up practice and are, therefore, a human resources issue for the entire health care system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".