Bibliographic record
Abstract
OBJECTIVE To explore the nature of professional stress and the strategies used by family physicians to deal with this stress. DESIGN Qualitative study. SETTING Kitchener-Waterloo, Ont. PARTICIPANTS Ten key-informant family physicians. METHODS In-depth interviews were conducted with key informants. A total of 40 key informants were identified, based on selected criteria; 24 provided consent. The potential participants were rank-ordered for interviews to provide maximum variation in age, sex, and years in practice. Interviews were conducted, audiotaped, transcribed verbatim, and analyzed until thematic saturation was reached, as determined through an iterative process. This occurred after 10 in-depth interviews. Immersion and crystallization techniques were used. MAIN FINDINGS The participants described professional stresses and strategies at the personal, occupational, and health care system levels. Personal stressors included personality traits and the need to balance family and career, which were countered by biological, psychological, social, and spiritual strategies. Occupational stressors included challenging patients, high workload, time limitations, competency issues, challenges of documentation and practice management, and changing roles within the workplace. Occupational stressors were countered by strategies such as setting limits, participating in continuing medical education, soliciting support from colleagues and staff, making use of teams, improving patient-physician relationships, exploring new forms of remuneration, and scheduling appropriately. Stressors affecting the wider health care system included limited resources, imposed rules and regulations, lack of support from specialists, feeling undervalued, and financial concerns. CONCLUSION Family physicians face a multitude of challenges at personal, occupational, and health care system levels. A systems approach provides a new framework in which proactive strategies can augment more than one level of a system and, in contrast, reactive strategies can have negative inputs for different system levels.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".