Factors Explaining Career Satisfaction among Psychiatrists and Surgeons in Canada
Bibliographic record
Abstract
BACKGROUND: The career satisfaction of specialists is affected by many variables ranging from family responsibilities, stress, the quality of services and facilities available to patients, professional rewards, and how the work is organized. OBJECTIVE: To articulate models that explain a substantial portion of the variance associated with career satisfaction among surgeons and psychiatrists in Canada. METHODS: Of 4958 eligible physicians across Canada, 2810 (56.7%) completed a 12-page survey between January and March 2004, following which the responding 148 surgeons and 231 psychiatrists were selected for this study. We checked response bias and found it was negligible. Hierarchical regression analysis was used 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 90.4% of the variance in career satisfaction for surgeons and 81.0% of the variance in career satisfaction for psychiatrists. The explanatory variables consisted of distress and coping, role in community activities, access to and quality of health care services, intrinsic and extrinsic rewards, workload, and organizational structure. CONCLUSIONS: The study demonstrated that variance associated with career satisfaction can be explained using various factors reported directly by physicians. The study also confirmed that relative differences in the importance of these factors do occur among specialties. Surgeons prefer to delegate more responsibility in the management of their practices on an informal basis, whereas psychiatrists prefer to be more involved in the management of their practices and use more formal structures.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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".