Pathways to translating experiential knowledge into mental health policy.
Bibliographic record
Abstract
OBJECTIVE: This research explored the pathways through which the experiential knowledge of people who need and use mental health and social housing services (citizen-users) gains access to policymaking. METHODS: Qualitative instrumental case study methodology focused the study on the policy field of mental health and social housing in Manitoba, Canada. Data collection included interviews with 21 key informants from four policy actor groups: citizen-users, service providers, advocacy organization representatives, and government officials. Relevant policy-related documents were also reviewed. Data were analyzed using inductive qualitative methods. RESULTS: Key informants described diverse pathways through which the experiential knowledge of citizen-users has been communicated to policy decision makers. Pathways have involved direct discourse between citizen-users and decision makers. Alternatively, indirect pathways were ones in which experiential knowledge was translated by other policy actors. Informants identified factors that could influence the integrity of the indirect pathways: the length and complexity of the pathways, the motivations and interests of the translators, and strategies to enhance the pathways. The pathways could be strengthened by developing the culture, leadership, knowledge, skills and attitudes supportive of engaging citizen-users and by accurately translating their experiential knowledge. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: If citizen-users are to be included in policymaking in a recovery-oriented mental health system, action must be taken to enhance the pathways through which their experiential knowledge reaches policymaking processes. Service providers, advocacy organization representatives and government officials can all take action to promote social policymaking that is informed by citizen-users' ideas and experiences.
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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 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.050 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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".