Choice and Partnership Approach to community mental health and addiction services: a realist-informed scoping review
Bibliographic record
Abstract
OBJECTIVES: The Choice and Partnership Approach (CAPA) was developed to create an accessible, child-centred and family-centred model of child and adolescent mental health service delivery that is adaptable to different settings. We sought to describe the state of evidence regarding the extent, outcomes and contextual considerations of CAPA implementation in community mental health services. DESIGN: Scoping review. DATA SOURCES: Published and grey literature were searched using MEDLINE, Embase, CINAHL, PsycINFO, Scopus and Google to 13 and 20 July 2022, respectively. ELIGIBILITY CRITERIA: We included reports focused on the implementation, outcomes (clinical, programme or system) or a discussion of contextual factors that may impact CAPA implementation in either child and adolescent or adult mental health services. DATA EXTRACTION AND SYNTHESIS: Data were extracted using a codebook that reflected the five domains of the Consolidated Framework for Implementation Research (CFIR) and reviewed for agreement and accuracy. Data were synthesised according to the five CFIR domains. RESULTS: Forty-eight reports describing 36 unique evaluations were included. Evaluations were observational in nature; 10 employed pre-post designs. CAPA implementation, regardless of setting, was largely motivated by long wait times. Characteristics of individuals (eg, staff buy-in or skills) were not reported. Processes of implementation included facilitative leadership, data-informed planning and monitoring and CAPA training. Fidelity to CAPA was infrequently measured (n=9/36) despite available tools. Health system outcomes were most frequently reported (n=28/36); few evaluations (n=7/36) reported clinical outcomes, with only three reporting pre/post CAPA changes. CONCLUSIONS: Gaps in evidence preclude a systematic review and meta-analysis of CAPA implementation. Measurement of clinical outcomes represents an area for significant improvement in evaluation. Consistent measurement of model fidelity is essential for ensuring the accuracy of outcomes attributed to its implementation. An understanding of the change processes necessary to support implementation would be strengthened by more comprehensive consideration of contextual factors.
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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 | 0.052 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".