Is the Road to Mental Health Paved With Good Incentives? Estimating the Population Impact of Physician Incentives on Mental Health Care Using Linked Administrative Data
Bibliographic record
Abstract
OBJECTIVES: The use of physician incentives to improve health care, in general, has been extensively studied but its value in mental health care has rarely been demonstrated. In this study the population-level impact of physician incentives on mental health care was estimated using indicators for receipt of counseling/psychotherapy (CP); antidepressant therapy (AT); minimally adequate counseling/psychotherapy; and minimally adequate antidepressant therapy. The incentives' impacts on overall continuity of care and of mental health care were also examined. MATERIALS AND METHODS: Monthly cohorts of individuals diagnosed with major depression were identified between January 2005 and December 2012 and their use of mental health services tracked for 12 months following initial diagnosis. Linked health administrative data were used to ascertain cases and measure health service use. Pre-post changes associated with the introduction of physician incentives were estimated using segmented regression analyses, after adjusting for seasonal variation. RESULTS: Physician incentives reversed the downward and upward trends in CP and AT. Five years postintervention, the estimated impacts in percentage points for CP, AT, minimally adequate counseling/psychotherapy, and minimally adequate antidepressant therapy were +3.28 [95% confidence interval (CI), 2.05-4.52], -4.47 (95% CI, -6.06 to -2.87), +1.77 (95% CI, 0.94-2.59), and -2.24 (95% CI, -4.04 to -0.45). Postintervention, the downward trends in continuity of care failed to reverse, but were disrupted, netting estimated impacts of +7.53 (95% CI, 4.54-10.53) and +4.37 (95% CI, 2.64-6.09) for continuity of care and of mental health care. CONCLUSIONS: The impact of physician incentives on mental health care was modest at best. Other policy interventions are needed to close existing gaps in mental health care.
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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 | 0.035 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".