Naloxone and the Inner City Youth Experience (NICYE): a community-based participatory research study examining young people’s perceptions of the BC take home naloxone program
Bibliographic record
Abstract
Abstract Background Take home naloxone (THN) programs reduce mortality by training bystanders to respond to opioid overdoses. Clinical observation by the health care team at the Inner City Youth (ICY) program indicated that young adults appeared to enthusiastically participate in the THN program and developed improved relationships with staff after THN training. However, we found a dearth of literature exploring the experiences of young adults with THN programs. This study set out to address this gap and identify suggestions from the young adults for program improvement. The primary research question was “How do street-involved young people experience the THN Program in Vancouver, BC?” Methods The study was undertaken at the ICY Program. Two peer researchers with lived experience of THN were recruited from ICY and were involved in all phases of the study. The peer researchers and a graduate student facilitated two focus groups and five individual interviews with ICY program participants using a semi-structured interview guide. Audio recordings were transcribed verbatim. The cut-up-and-put-in-folders approach was used to identify emerging themes. Results The themes that emerged were perceptions of risk, altruism, strengthening relationship with staff, access to training, empowerment, and confidence in ability to respond, and suggestions for youth-friendly training. These themes were then situated within the framework of the health belief model to provide additional context. Participants viewed themselves as vulnerable to overdose and spoke of the importance of expanding access to THN training. Following training, participants reported an increase in internal locus of control, an improved sense of safety among the community of people who use drugs, improved self-esteem, and strengthened relationships with ICY staff. Overall, participants found THN training engaging, which appeared to enhance participation in other ICY programming. Conclusions Young people perceived THN training as a positive experience that improved relationships with staff. Participant recommendations for quality improvement were implemented within the provincial program.
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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 | 0.008 | 0.007 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".