Incentives and disincentives for treating of depression and anxiety in Ontario Family Health Teams: protocol for a grounded theory study
Bibliographic record
Abstract
INTRODUCTION: There is strong consensus that prevention and management of common mental disorders (CMDs) should occur in primary care and evidence suggests that treatment of CMDs in these settings can be effective. New interprofessional team-based models of primary care have emerged that are intended to address problems of quality and access to mental health services, yet many people continue to struggle to access care for CMDs in these settings. Insufficient attention directed towards the incentives and disincentives that influence care for CMDs in primary care, and especially in interprofessional team-based settings, may have resulted in missed opportunities to improve care quality and control healthcare costs. Our research is driven by the hypothesis that a stronger understanding of the full range of incentives and disincentives at play and their relationships with performance and other contextual factors will help stakeholders identify the critical levers of change needed to enhance prevention and management of CMDs in interprofessional primary care contexts. Participant recruitment began in May 2016. METHODS AND ANALYSIS: An explanatory qualitative design, based on a constructivist grounded theory methodology, will be used. Our study will be conducted in the Canadian province of Ontario, a province that features a widely implemented interprofessional team-based model of primary care. Semistructured interviews will be conducted with a diverse range of healthcare professionals and stakeholders that can help us understand how various incentives and disincentives influence the provision of evidence-based collaborative care for CMDs. A final sample size of 100 is anticipated. The protocol was peer reviewed by experts who were nominated by the funding organisation. ETHICS AND DISSEMINATION: The model we generate will shed light on the incentives and disincentives that are and should be in place to support high-quality CMD care and help stimulate more targeted, coordinated stakeholder responses to improving primary mental healthcare quality.
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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 | 0.088 | 0.059 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.070 | 0.008 |
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".