“The system always undermined what I was trying to do as an individual”: identifying opportunities to improve the delivery of opioid use services for youth from the perspective of service providers in four communities across British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Substance use among youth is a longstanding global health concern that has dramatically risen in the era of highly toxic and unregulated drugs, including opioids. It is crucial to ensure that youth using unregulated opioids have access to evidence-based interventions, and yet, youth encounter critical gaps in the quality of such interventions. This study aims to address these gaps by identifying opportunities to improve the quality of opioid use services from the perspective of service providers, a perspective that has received scant attention. METHODS: This community-based participatory study was conducted in four communities in British Columbia (Canada), a province that declared a public health overdose emergency in 2016. Human-centered co-design workshops were held to understand service providers' (n = 41) experiences, needs, and ideas for improving the quality of youth opioid use services/treatments in their community. Multi-site qualitative analysis was used to develop overarching experiences and needs themes that were further contextualized in each local community. A blended deductive and inductive thematic analysis was used to analyze the ideas data. RESULTS: Three overarching themes were identified, reflecting service providers' goals to respond to youth in a timely and developmentally appropriate manner. However, this was significantly limited by organizational and systems-level barriers, revealing service providers' priorities for intra- and inter-organizational support and collaboration and systems-level innovation. Across communities, service providers identified 209 individual ideas to address these prioritized needs and improve the quality of youth opioid use services/treatments. CONCLUSION: These themes demonstrate a multi-level tension between macro-level systems and the meso-level organization of youth opioid use services, which undermine the quality of individual-level care service providers can deliver. These findings underscore the need for a coordinated multi-level response, such as developing youth-specific standards (macro-level), increasing inter-organizational activities and collaboration (meso-level), and creating programs that are specific to youths' needs (micro-level).
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.017 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".