“It's an emotional roller coaster… But sometimes it's fucking awesome”: Meaning and motivation of work for peers in overdose response environments in British Columbia
Bibliographic record
Abstract
BACKGROUND: The province of British Columbia (BC), Canada is amid dual public health emergencies in which the overdose epidemic declared in 2016 has been exacerbated by restrictions imposed by the Coronavirus Disease of 2019 (COVID-19) pandemic. Experiential workers, commonly known as 'peers' (workers with past or present drug use experience) are at the forefront of overdose response initiatives and are essential in creating safe spaces for people who use drugs (PWUD) in harm reduction. Working in overdose response environments can be stressful, with lasting emotional and mental health effects. There is limited knowledge about the personal meaning that experiential workers derive from their work, which serve as motivators for them to take on these often-stressful roles. METHODS: This project used a community-based qualitative research design. The research was based at two organizations in BC. Eight experiential worker-led focus groups were conducted (n = 31) where participants spoke about their roles, positive aspects of their jobs, challenges they face, and support needs in harm reduction work. Transcripts were coded and analyzed using interpretative description to uncover the meaning derived from experiential work. RESULTS: Three themes emerged from focus group data that describe the meanings which serve as motivators for experiential workers to continue working in overdose response environments: (1) A sense of purpose from helping others; (2) Being an inspiration for others, and; (3) A sense of belonging. CONCLUSION: Despite the frequent hardships and loss that accompany overdose response work, experiential workers identified important aspects that give their work meaning. These aspects of their work may help to protect workers from the emotional harms associated with stressful work as well as the stigma of substance use. Recognizing the importance of experiential work and its role in the lives of PWUD can help inform and strengthen organizational supports.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.017 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.008 |
| 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".