Community mental health funding, stakeholder engagement and outcomes: a realist synthesis
Bibliographic record
Abstract
Mental health services continues to be a high priority for healthcare and social service systems. Funding structures within community mental health settings have shown to impact service providers' behaviour and practices. Additionally, stakeholder engagement is suggested as an important mechanism to achieving the intended goals. However, the literature on community mental health funding reform and associated outcomes is inconsistent and there are no consistent best practices for stakeholder engagement in such efforts. OBJECTIVES: This study sought to understand how stakeholder engagement impacts outcomes when there is a change in public funding within community mental health settings. DESIGN: A realist synthesis approach was used to address the research question to fully understand the role of stakeholder engagement as a mechanism in achieving outcomes (system and service user) in the context of community mental health service reform. An iterative process was used to identify programme theories and context-mechanism-outcome configurations within the literature. RESULTS: Findings highlight that in the absence of stakeholder engagement, funding changes may lead to negative outcomes. When stakeholders were engaged in some form, funding changes were more often associated with positive outcomes. Stakeholder engagement is multifaceted and requires considerable time and investment to support achieving intended outcomes when funding changes are implemented. CONCLUSIONS: To support successful transformation of community mental health programmes, it is important that stakeholders are meaningfully engaged during funding allocation changes. Stakeholder engagement may entail connecting around a shared purpose, individual participation and meaningful interactions and dialogue.
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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 | 0.109 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".