"Running myself ragged": Stressors Faced by Peer Workers in Overdose Response Settings
Bibliographic record
Abstract
Abstract Background: Peer workers or ‘peers’ (workers with past or present drug use experience) are at the forefront of overdose response initiatives and their role is essential in creating safe spaces for people who use drugs (PWUD). Working in overdose response settings has benefits for peer workers but is also stressful, with lasting emotional and mental health effects. Yet, little is known about the stressors peer workers face and what interventions can be implemented to support them in their roles. Methods: This project used a community-based sequential mixed methods research design. Eight peer researcher-led focus groups (n=31) were conducted between November 2018 and March 2019 to assess needs of peer workers. The transcripts were thematically coded and analyzed using interpretative description. These results informed a survey which was conducted (n=50) in September 2019 to acquire quantitative data on peer workers’ perception of health, quality of life, working conditions and stressors. Frequency distributions were used to describe characteristics of participants. X 2 distribution values with Yates correction were conducted to check for association between variables. Results: Five themes emerged from the focus groups that point to stressors felt by peer workers: (1) Financial insecurity; (2) lack of respect and recognition at work; (3) housing challenges; (4) inability to access and/or refer individuals to resources; and (5) constant exposure to death and trauma. Consistent with this, the factors that survey participants picked as one of their “top three stressors” included financial situation, work situation, and housing challenges. Conclusion: Peer workers are faced with a diversity of stressors in their lives which often reflect societal stigmatization of drug use. Recognition of these systemic stressors is critical in designing interventions to ease the emotional, physical and financial burden faced by peer workers.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".