‘On the same level’: facilitators’ experiences running a drug user-led safer injecting education campaign
Bibliographic record
Abstract
BACKGROUND: Unsafe injection practices play a major role in elevated rates of morbidity and mortality among people who inject drugs (IDU). There is growing interest in the direct involvement of IDU in interventions that seek to address unsafe injecting. This study describes a drug user-led safer injecting education campaign, and explores facilitators' experiences delivering educational workshops. METHODS: We conducted semi-structured qualitative interviews with 8 members of the Injection Support (IS) Team who developed and facilitated a series of safer injecting education workshops. Interviews explored facilitator's perceptions of the workshops, experiences being a facilitator, and perspectives on the educational campaign. Interviews were transcribed verbatim and a thematic analysis was conducted. RESULTS: IS Team facilitators described how the workshop's structure and content enabled effective communication of information about safer injecting practices, while targeting the unsafe practices of workshop participants. Facilitators' identity as IDU enhanced their ability to relate to workshop participants and communicate educational messages in language accessible to workshop participants. Facilitators reported gaining knowledge and skills from their involvement in the campaign, as well as positive feelings about themselves from the realization that they were helping people to protect their health. Overall, facilitators felt that this campaign provided IDU with valuable information, although facilitators also critiqued the campaign and suggested improvements for future efforts. CONCLUSIONS: This study demonstrates the feasibility of involving IDU in educational initiatives targeting unsafe injecting. Findings illustrate how IDU involvement in prevention activities improves relevance and cultural appropriateness of interventions while providing individual, social, and professional benefits to those IDU delivering education.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".