Weaving community-based participatory research and co-design to improve opioid use treatments and services for youth, caregivers, and service providers
Bibliographic record
Abstract
Integrating the voices of service users and providers in the design and delivery of health services increases the acceptability, relevance, and effectiveness of services. Such efforts are particularly important for youth opioid use treatments and services, which have failed to consider the unique needs of youth and families. Applying community-based participatory research (CBPR) and co-design can facilitate this process by contextualizing service user experiences at individual and community levels and supporting the collaborative design of innovative solutions for improving care. However, few studies demonstrate how to effectively integrate these methods and engage underserved populations in co-design. As such, this manuscript describes how our team wove CBPR and co-design methods to develop solutions for improving youth opioid use treatments and services in Canada. As per CBPR methods, national, provincial, and community partnerships were established to inform and support the project's activities. These partnerships were integral for recruiting service users (i.e., youth and caregivers) and service providers to co-design prototypes and support local testing and implementation. Co-design methods enabled understanding of the needs and experiences of youth, caregivers, and service providers, resulting in meaningful community-specific innovations. We used several engagement methods during the co-design process, including regular working group meetings, small group discussions, individual interviews and consultations, and feedback grids. Challenges involved the time commitment and resources needed for co-design, which were exacerbated by the COVID-19 pandemic and limited our ability to engage a diverse sample of youth and caregivers in the process. Strengths of the study included youth and caregiver involvement in the co-design process, which centered around their lived experiences; the therapeutic aspect of the process for participants; and the development of innovations that were accepted by design partners.
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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 | 0.160 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".