Physician Incentives and Sex/Gender Differences in Depression Care: An Interrupted Time Series Analysis
Why this work is in the frame
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Bibliographic record
Abstract
Introduction: Physician incentives have been shown to reduce socioeconomic disparities in health care. The impact on sex/gender inequalities, however, has rarely been investigated. Using population-based data, this study investigated sex/gender differences in depression care and the impact of physician incentives. Methods: Deidentified health data from physician claims, hospitals, vital statistics, prescription database, and insurance plan registries in British Columbia, Canada, were examined, retrospectively. Individuals with depression were identified and their use of mental health services was tracked for 12 months following initial diagnosis. The following indicators were assessed: (1) counseling/psychotherapy (CP), (2) minimally adequate counseling/psychotherapy (MACP), (3) antidepressant therapy (AT), and (4) minimally adequate antidepressant therapy (MAAT). Sex/gender differences in these indicators before (January 2005–December 2007) and after (January 2008–December 2012) the introduction of physician incentives were estimated using interrupted time series analysis. Results: Preintervention, the percentage of individuals with depression who received CP was higher among males (CP: 58.4%, MACP: 13.6%) than females (CP: 57.1%, MACP: 10.9%). In contrast, the percentage who received AT was higher among females (AT: 57.7%, MAAT: 47.4%) than males (AT: 53.6%, MAAT: 41.9%). These statistically significant sex/gender differences remain unchanged postintervention. Conclusions: Sex/gender differences in depression care persist despite the introduction of physician incentives.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it