Developing an Evaluation Framework for Consumer-Centred Collaborative Care of Depression Using Input from Stakeholders
Bibliographic record
Abstract
OBJECTIVE: To develop a framework for research and evaluation of collaborative mental health care for depression, which includes attributes or domains of care that are important to consumers. METHODS: A literature review on collaborative mental health care for depression was completed and used to guide discussion at an interactive workshop with pan-Canadian participants comprising people treated for depression with collaborative mental health care, as well as their family members; primary care and mental health practitioners; decision makers; and researchers. Thematic analysis of qualitative data from the workshop identified key attributes of collaborative care that are important to consumers and family members, as well as factors that may contribute to improved consumer experiences. RESULTS: The workshop identified an overarching theme of partnership between consumers and practitioners involved in collaborative care. Eight attributes of collaborative care were considered to be essential or very important to consumers and family members: respectfulness; involvement of consumers in treatment decisions; accessibility; provision of information; coordination; whole-person care; responsiveness to changing needs; and comprehensiveness. Three inter-related groups of factors may affect the consumer experience of collaborative care, namely, organizational aspects of care; consumer characteristics and personal resources; and community resources. CONCLUSION: A preliminary evaluation framework was developed and is presented here to guide further evaluation and research on consumer-centred collaborative mental health care for depression.
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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 | 0.291 | 0.150 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".