Implementing a knowledge application program for anxiety and depression in community-based primary mental health care: a multiple case study research protocol
Bibliographic record
Abstract
BACKGROUND: Anxiety and depressive disorders are increasingly recognized as a health care policy priority. Reducing the treatment gap for common mental disorders requires strengthening the quality of primary mental health care. We developed a knowledge application program designed to improve the organization and delivery of care for anxiety and depression in community-based primary mental health care teams in Quebec, Canada. The principal objectives of the study are: to implement and evaluate this evidence-based knowledge application program; to examine the contextual factors associated with the selection of local quality improvement strategies; to explore barriers and facilitators associated with the implementation of local quality improvement plans; and to study the implementation of local quality monitoring strategies. METHODS: The research design is a mixed-methods prospective multiple case study. The main analysis unit (cases) is composed of the six multidisciplinary community-based primary mental health care teams, and each of the cases has identified at least one primary care medical clinic interested in collaborating with the implementation project. The training modules of the program are based on the Chronic Care Model, and the implementation strategies were developed according to the Promoting Action on Research Implementation in Health Services conceptual framework. DISCUSSION: The implementation of an evidence-based knowledge application program for anxiety and depression in primary care aims to improve the organization and delivery of mental health services. The uptake of evidence to improve the quality of care for common mental disorders in primary care is a complex process that requires careful consideration of the context in which innovations are introduced. The project will provide a close examination of the interplay between evidence, context and facilitation, and contribute to the understanding of factors associated with the process of implementation of interventions in routine care. The implementation of the knowledge application program with a population health perspective is consistent with the priorities set forth in the current mental health care reform in Quebec. Strengthening primary mental health care will lead to a more efficient health care system.
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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 | 0.104 | 0.052 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".