Planning the implementation of change based on collaborative care model evidence for people with common mental disorders and physical long-term conditions: a case example
Bibliographic record
Abstract
Rationale, aims and objectives: There are many challenges to implementing the collaborative care model (CCM) for people with common mental disorders and physical long-term conditions in primary care settings. There is also a knowledge gap on how to implement change based on CCM evidence. This article aim to present a case example of a process to plan the implementation of change based on CCM evidence in primary care settings. Context of the case example: The process of planning the implementation of change was conducted during a multiple case study in three family medicine groups in Quebec, Canada. The pre-implementation steps of the Grol & Wensing implementation of change model were used to design the planning process. Process to plan the implementation of change: 1) review of the literature on the CCM, engaging stakeholders, development of data collection tools; 2) familiarization with actual collaborative care process and professional activities, assessment of the quality of activities with analysis tables; 3) identification of barriers and enablers to implement change, visualization of the results, prioritization of potential strategies with an advisory committee; 4) validation of results and assessment of practices, selection and development of strategies tailored to local needs. Various data sources have been used: feedback from managers, advisory committee and local working groups, interviews (n=32), observations (n=7), documents, and schemas. Conclusion: Planning the implementation of change based on CCM evidence helped select strategies tailored to local needs that might overcome determinants of change impacting the quality of activities and the team’s capacity to efficiently implement change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".