Implementing a Continuum of Evidence-Based Psychosocial Interventions for People with Severe Mental Illness: Part 2—Review of Critical Implementation Issues
Bibliographic record
Abstract
OBJECTIVE: In North America and internationally, efforts have been made to reduce the gaps between knowledge of psychosocial evidence-based practices (EBPs) and the delivery of such services in routine mental health practice. Part 2 of this review identifies key issues for stakeholders to consider when implementing comprehensive psychosocial EBPs for people with severe mental illness (SMI). METHOD: A rapid review of the literature was conducted. Searches were carried out in MEDLINE and PsycINFO for reports published between 1990 and 2012 using key words related to SMI, and psychosocial practices and implementation. The Consolidated Framework for Implementation Research (CFIR) was used to structure findings according to key domains and constructs known to influence the implementation process. RESULTS: The CFIR allowed us to identify 17 issues reflecting more than 30 constructs of the framework that were viewed as influential to the process of implementing evidence-based psychosocial interventions for people with SMI. Issues arising at different levels of influence (intervention, individual, organizational, and system) and at all phases of the implementation process (planning, engagement, execution, and evaluation) were found to play important roles in implementation. CONCLUSION: The issues identified in this review should be taken into consideration by stakeholders when engaging in efforts to promote uptake of new psychosocial EBPs and to widen the range of effective psychosocial services available in routine mental health 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 | 0.120 | 0.238 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".