Gender differences in career dissatisfaction among Pennsylvanian physicians
Bibliographic record
Abstract
Objective: The physician workforce is quickly changing from one that was once male dominated to one that is more gender equal. The relationship between being female and physician career satisfaction is unclear despite a large body of research on the subject. I analyze the relationship between gender, career dissatisfaction, and plans to leave patient care. Female-male differences are calculated for various demographic, specialty, and practice setting subgroups of physicians; particular attention is paid to how various factors interact with gender.Methods: Data comes from the 2012 Pennsylvania Health Workforce Survey of Physicians. I use multivariate, logistic regression to estimate associations between a number of covariates, including gender, and two outcomes: (1) career dissatisfaction, and (2) plans to leave patient care.Results: Female physicians have 12% lower odds than males of reporting career dissatisfaction but no statistically significant difference in plans to leave patient care. Practicing in a hospital setting and in a rural county is associated with higher odds of dissatisfaction among male physicians but lower dissatisfaction among female physicians. Although female physicians own their practice at much lower rates, female owners have much lower odds of planning to leave patient care.Conclusions: Factors associated with career dissatisfaction and plans to leave patient care affect male and female physicians differently, across race, rural practice, specialty, and practice ownership. Policy and research related to physician retention and quality of care should consider the interaction between gender and these factors in the future.
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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 | 0.000 | 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".