Cornerstones of Career Satisfaction in Medicine
Bibliographic record
Abstract
OBJECTIVES: To establish a reliable and concise measure of career satisfaction that covers all 4 of its dimensions and to document higher dimensions of satisfaction among the major medical specialties and across varying patterns of clinical practice. METHODS: In 2004, we conducted a stratified, cross-sectional survey of physicians in Canada. Of the eligible population, 2810 physicians (56.7%) responded. We checked response bias and found it was negligible. Responding physicians completed a 17-item measure of career satisfaction along with a detailed breakdown of clinical, academic, and administrative duties. We used confirmatory factor analysis to verify the existence of the hypothesized dimensions of higher-order satisfaction. We then used Scheffe's tests to document differences in the levels of all satisfaction dimensions, both among specializations and by clinical practice profile. RESULTS: Factor analysis revealed 4 reliable dimensions of satisfaction: personal (alpha = 0.85), professional (alpha = 0.78), inherent (alpha = 0.70), and performance (alpha = 0.75). Inherent satisfaction with medicine as a career was the most important dimension for all specializations and for all patterns of practice. The addition of administrative duties without a reduction of clinical duties compromised personal, professional, and performance dimensions of career satisfaction. Academic duties contributed significantly to most physicians' overall, inherent, and performance satisfaction. CONCLUSION: Distinguishing higher-order dimensions of satisfaction from basic ones is a groundbreaking finding because addressing higher-order dimensions supports self-actualization and superior performance of duties.
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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 | 0.002 | 0.009 |
| 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".