Strategies for engaging patients and families in collaborative care programs for depression and anxiety disorders: A systematic review
Bibliographic record
Abstract
BACKGROUND: Patients and families are often referred to as important partners in collaborative mental health care (CMHC). However, how to meaningfully engage them as partners remains unclear. We aimed to identify strategies for engaging patients and families in CMHC programs for depression and anxiety disorders. METHODS: We updated a Cochrane review of CMHC programs for depression and anxiety disorders. Searches were conducted in Cochrane CCDAN and CINAHL, complemented by additional database searches, trial registry searches, and cluster searches for 'sibling' articles. Coding and data extraction of engagement strategies was an iterative process guided by a conceptual framework. We used narrative synthesis and descriptive statistics to report on findings. FINDINGS: We found 148 unique CMCH programs, described in 578 articles. Most programs (96%) featured at least one strategy for engaging patients or families. Programs adopted 15 different strategies overall, with a median of two strategies per program (range 0-9 strategies). The most common strategies were patient education (87% of programs) and self-management supports (47% of programs). Personalized care planning, shared decision making, and family or peer supports were identified in fewer than one third of programs. LIMITATIONS: Our search strategy was designed to capture programs evaluated in clinical trials and so other innovative programs not studied in trials were likely missed. CONCLUSION: Most CMHC programs for depression and anxiety disorders adopted a limited number of strategies to engage patients and families in their care. However, this review identifies numerous strategies that can be used to strengthen the patient- and family-centeredness of collaborative 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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".