Conceptualizing the outcomes of involving people who use mental health services in policy development
Bibliographic record
Abstract
CONTEXT: Inclusion of people who use mental health services in policymaking is a goal of many mental health systems. However, the outcomes of such involvement have not been well articulated or researched. OBJECTIVES: The objectives of this research were to explore how the social and personal outcomes of citizen-user involvement in mental health policymaking were conceptualized by policy actors and to create a conceptual framework to guide the development and evaluation of citizen-user involvement. DESIGN: This qualitative instrumental case study explored the phenomenon of citizen-user involvement using the policy field of mental health and social housing policy in the Province of Manitoba, Canada, as the focal case. PARTICIPANTS: A total of 21 informants from four policy actor groups, citizen-users, representatives of advocacy organizations, government officials and service providers, participated in key informant interviews. Data also included policy documents relevant to the policy field. ANALYSIS: Data collected from interviews and policy documents were analysed using an inductive qualitative paradigm. RESULTS: Participants identified multiple outcomes of citizen-user involvement in policymaking. The resulting conceptual framework illustrated how outcomes in personal, substantive, instrumental and normative dimensions influence micro-, meso- and macrosocial structures. The results also provided a cautionary tale by suggesting how attention needs to be paid to managing the risks as well as optimizing the rewards of involvement. CONCLUSIONS: The framework has application in guiding the development and evaluation of mechanisms that aim to involve citizen-users in policymaking. The framework encourages an approach that takes into account the complexity and multidimensional nature of engaging citizen-users.
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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 | 0.070 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.022 | 0.085 |
| Scholarly communication | 0.023 | 0.024 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".