Predictors of Professional and Personal Satisfaction with a Career in Psychiatry
Bibliographic record
Abstract
BACKGROUND: Many factors, including personal experience and personality traits, contribute to the emotional difficulties that psychiatrists experience in their professional work. The nature of the work itself also plays a significant role. OBJECTIVE: To determine those personal and professional characteristics that predict satisfaction with the practice of psychiatry. METHOD: We mailed a questionnaire that included items pertaining to aspects of personal and professional life to the entire population of psychiatrists in Ontario (N = 1574). RESULTS: Of the 1574, 52% (n = 802) responded. We conducted a series of regression analyses to determine factors related to career satisfaction or regret. A belief in the intrinsic value of psychiatry, a low perceived degree of emotional burden from patients, financial success, and satisfaction with psychotherapeutic work emerged consistently as significant predictors. A subsequent discriminant function analysis indicated that all 4 of these variables accurately predicted those psychiatrists with extreme satisfaction or dissatisfaction with work. CONCLUSIONS: These results reveal several variables associated with career satisfaction in the practice of psychiatry that might be useful to discuss with residents who are beginning their careers.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".