Incentives and Disincentives for the Treatment of Depression and Anxiety: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: There is widespread support for primary care to help address growing mental health care demands. Incentives and disincentives are widely used in the design of health care systems to help steer toward desired goals. The absence of a conceptual model to help understand the range of factors that influence the provision of primary mental health care inspired a scoping review of the literature. Understanding the incentives that promote and the disincentives that deter treatment for depression and anxiety in the primary care context will help to achieve goals of greater access to mental health care. METHOD: A review of the literature was conducted to answer the question, how are incentives and disincentives conceptualized in studies investigating the treatment of common mental disorders in primary care? A comprehensive search of MEDLINE, PsycINFO, CINAHL, and Google Scholar was undertaken using Arksey and O'Malley's 5-stage methodological framework for scoping reviews. RESULTS: We identified 27 studies. A range of incentives and disincentives influence the success of primary mental health care initiatives to treat depression and anxiety. Six types of incentives and disincentives can encourage or discourage treatment of depression and anxiety in primary care: attitudes and beliefs, training and core competencies, leadership, organizational, financial, and systemic. CONCLUSIONS: Understanding that there are 6 different types of incentives that influence treatment for anxiety and depression in primary care may help service planners who are trying to promote improved 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 | 0.034 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".