More Women are Medicated While More Men are Talked Out: Persistent Gender Disparities in Mental Health Care
Bibliographic record
Abstract
Introduction Physician incentives have been shown in previous studies to help reduce socioeconomic disparities in health care. Its impact on gender disparities, however, has rarely been investigated. Aim The impact of physician incentives on gender disparities in mental health care was investigated in this retrospective study. Method De-identified health administrative data from physician claims, hospital separations, vital statistics, prescription database, and insurance plan registries were linked and examined. Monthly cohorts of individuals with depression who were residing in British Columbia, Canada were identified and their use of mental health services tracked for 12 months following receipt of initial diagnosis. indicators that assess receipt of the following services were created: – counseling/psychotherapy (CP); – minimally adequate counseling/psychotherapy (MACP); – antidepressant therapy (AT); – minimally adequate antidepressant therapy (MAAT). interrupted time series analysis was used to estimate changes in these indicators before (01/2005–12/2007) and after (01/2008–12/2012) physician incentives were introduced. Results At the beginning of the study period, the percentage of individuals diagnosed with depression who received counseling/psychotherapy was higher, on average, among men (CP: 58.4%, MACP: 13.6%) than women (CP: 57.1%, MACP: 10.9%). in contrast, the percentage who received antidepressant therapy was higher among women (AT: 57.7%, MAAT: 47.4%) than men (AT: 53.6%, MAAT: 41.9%). Levels for these indicators have changed over time but the statistically significant differences between men and women were virtually the same before and after incentives were introduced. Conclusions Gender disparities in mental health care persist despite the introduction of physician incentives designed to enhance access to mental health services in primary care. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".