Meaningful inclusion of consumers in research and service delivery.
Bibliographic record
Abstract
OBJECTIVE: Although participatory methods have become increasingly popular, people with lived experience of mental illness and homelessness have been historically excluded from service planning and research. To better plan for meaningful inclusion of consumers, this study examines lessons learned from the People with Lived Experience Caucus in the Toronto Site of the At Home/Chez Soi Research Demonstration Project on Homelessness and Mental Health. METHOD: The inclusion of the People with Lived Experience Caucus was evaluated using qualitative methods and multiple data sources, including review of 42 documents, 11 individual interviews, and three focus groups. Caucus members were included in the study team. Transcripts were analyzed using grounded theory methodology. RESULTS: Findings revealed a complex story of Caucus engagement: Facing time constraints and given little direction, the Caucus developed through a tumultuous process related to both internal and external barriers to meaningful inclusion. Despite the challenges, the Caucus contributed meaningfully to various aspects of the research demonstration project. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: It is possible to successfully integrate psychiatric consumers with experience of homelessness in many aspects of research and service planning. Suggestions for future initiatives hoping to engage consumers include: early involvement, purposeful selection of members, clear communication of roles and responsibilities, a consumer coordinating group, and space for critical dialog throughout the process. Lessons learned can inform the inclusion of consumers in similar endeavors in other jurisdictions.
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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 | 0.124 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".