Factors associated with career satisfaction among general practitioners in Canada.
Bibliographic record
Abstract
INTRODUCTION: There are important differences in rural, regional and urban general practice environments. The purpose of this study was to articulate models that explain career satisfaction among general practitioners (GPs) in these practice environments. METHODS: Of 4958 eligible physicians across Canada, 2810 (56.7%) completed a 12- page survey between January and March 2004, from whom a total of 256 GPs in rural, regional and urban communities were selected. Response bias was checked and found to be negligible. We used hierarchical regression analysis to record cumulative R2, standardized beta and significance levels as each predictor was entered. We applied weighting factors to reflect the actual physician population in Canada. RESULTS: The models explained 88.5% of the variance in career satisfaction for GPs in small towns, 88.9% for GPs in regional communities and 86.3% for GPs in urban cities. The explanatory variables consisted of distress and coping, role in community activities, the quality of health care services and access to them, intrinsic and extrinsic rewards, workload and organizational structure. CONCLUSION: Career satisfaction for small-town doctors is associated with being able to cope with stress in handling a wide variety of clinical conditions, largely on their own, but with effective collaboration from physicians in larger centres. Rural GPs also enjoy academic responsibilities. Satisfaction for GPs in regional communities also depends on coping with stress and the ability to maintain an efficiently operating set of secondary- level health services in their community. Satisfaction for urban GPs is associated with collegiality, which dampens stress, and access to a full range of health services, including community, hospital, mental health and rehabilitation services. Career satisfaction for all GPs is associated with equity, manageable workloads and effective practice management; however, all of these professional issues contribute, in small increments, to satisfaction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".