The Cost of Caring: Compassion Fatigue Among Peer Workers in Overdose Response Settings in British Columbia
Bibliographic record
Abstract
Abstract Background The drug toxicity crisis has had dramatic impacts upon communities of people who use substances. Peer workers, individuals with lived/living experience of substance use who work in overdose response settings, are particularly susceptible to negative impacts on wellbeing caused by this crisis. Coupled with the devastating effects of the COVID-19 pandemic including reduced capacity and hours of overdose prevention services and physical distancing regulations, the burden placed upon peers is large. However, these mental health impacts have yet to be studied and measured. Methods The Professional Quality of Life Scale survey (Version 5) was taken by 47 peer workers between September 2020 and March 2021 to assess compassion satisfaction and compassion fatigue. It was administered as a part of a larger survey administered by peer research assistants - to evaluate the effectiveness of interventions identified and implemented through a peer-led project. Some questions from the tool were also asked prior to implementation of the intervention (September 2020). Participants were recruited by their organizational managers and paid a $25 honorarium. Results Our study uncovered a HIGH mean score for compassion satisfaction, a LOW mean score for burnout, and a MEDIUM mean score for secondary traumatic stress among peers working in overdose response settings in British Columbia. We also found changes before and after implementation of the intervention. After implementation, peer workers felt more satisfied from their work, more connected to others, less worn out and were less affected by the traumatic stress of those they help. Conclusion Although peers derive significant pleasure and fulfillment from their jobs, i.e., compassion satisfaction, they also face considerable feelings of overwhelmingness, i.e. burnout, and stress due to continuous exposure to the trauma of the people they support, i.e. secondary traumatic stress. These results lay the groundwork for further research on the intersectional factors contributing to negative mental health impacts upon peer workers and highlight potential strategies that bolster the fulfillment they derive from their jobs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".