Implementing a Continuum of Evidence-Based Psychosocial Interventions for People with Severe Mental Illness: Part 1—Review of Major Initiatives and Implementation Strategies
Bibliographic record
Abstract
OBJECTIVE: Providing comprehensive care to people with severe mental illness (SMI) involves moving beyond pharmacological treatment and ensuring access to a wide range of evidence-based psychosocial services. Numerous initiatives carried out in North America and internationally have promoted the widespread adoption of such services. Objectives of this rapid review were 3-fold: to identify these implementation initiatives, to describe the implementation strategies used to promote the uptake of psychosocial services, and to identify key issues related to the implementation of a broad range of services. Part 1 presents findings for objectives 1 and 2 of the review. METHOD: Searches were carried out in MEDLINE and PsycINFO for reports published between 1990 and 2012 using key words related to SMI, psychosocial practices, and implementation. Contacts with experts and reference list and reverse citation searches were also conducted. RESULTS: Fifty-five articles were retained that identified more than a dozen major North American and international implementation initiatives. Initiative leaders employed diverse strategies at the planning, execution, and evaluation stages of the implementation process. Stakeholder meetings, training, ongoing consultation, and quality or fidelity monitoring were strategies consistently adopted across most initiatives, whereas theory-based approaches and organizational- and system-level strategies were less frequently described. CONCLUSION: Insights from the initiatives identified in this review can help guide future efforts to implement a broad range of psychosocial services for people with SMI. However, such efforts will also need to be informed by more rigorous, theory-based studies of implementation processes and outcomes.
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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 | 0.036 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".