Having a voice and saving lives: a qualitative survey on employment impacts of people with lived experience of drug use working in harm reduction
Bibliographic record
Abstract
Background: Ongoing legal and social discrimination, and stigmatization of people with lived experience of drug use (PWLE) continues to contribute to overdose-related deaths in Canada. The involvement of PWLE working in harm reduction services has proven effective in decreasing drug-related harms among PWLE; however, there exist unintended negative impacts. PWLE working in harm reduction services risk overextending themselves beyond employment parameters (e.g., emotional labor) with few systems in place (e.g., employment advocacy) for support. While meaningful participation of PWLE in harm reduction programs is critical to addressing the overdose crisis, their labor in Canada’s overdose response commands further investigation and recognition. This paper examines some of the benefits and negative aspects of working in harm reduction among PWLE. Methods: Fifty qualitative surveys were completed by PWLE working in harm reduction services from across Canada at the National 2018 Stimulus conference held in Edmonton, Alberta. The surveys focused on the benefits and negatives of ‘peer’ employment and recommendations for organizational transformation through short answer written sections. Surveys were analyzed thematically using NVivo, informed by critical perspectives on substance use, with attention to key re-occurring themes on employment equity. Results: While participants described multiple benefits of working in harm reduction services, such as the valuing of their expertise by fellow ‘peers,’ growing skill sets, countering stigma, and preventing overdose deaths, issues of workplace equity were significantly identified. Stigma, tokenism, workplace discrimination, including power and pay inequities, as well as lack of worker compensation and benefits were identified as key factors persisting in the everyday experiences of participants. Conclusion: Continued exposure to stigma, workplace discrimination, and/or power imbalances, combined with the impact of high stakes employment (e.g., dealing with overdose deaths), can have significant consequences for PWLE working in harm reduction, including burn out. Policy recommendations include large-scale structural changes that address inequities of hierarchical ‘peer’ employment for PWLE, including increased leadership roles for diverse PWLE, pay equity and benefits, unionization, as well as more supportive working environments attentive to the intersecting social-structural factors (poverty, criminalization, racism, gendered violence) impacting the everyday lives of PWLE working in harm reduction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".