The Cost of Caring: Compassion Fatigue among Peer Overdose Response Workers in British Columbia
Bibliographic record
Abstract
Background: The drug toxicity crisis has had dramatic impacts on people who use drugs. Peer overdose response workers (peer responders), i.e., individuals with lived/living experience of drug use who work in overdose response settings, are particularly susceptible to negative physical and mental health impacts of the crisis. Despite that, the mental health impacts on peer responders have yet to be studied and measured. Methods: The Professional Quality of Life survey (Version 5) was completed by 47 peer responders at two organizations in British Columbia between September 2020 and March 2021 to assess compassion satisfaction and compassion fatigue. The Likert scale responses were converted into numerical values and scores were calculated for each sub-scale. The mean score was calculated for each sub-scale and categorized as low, medium, or high, based on the instructions for Version 5 of the instrument. Results: Our study uncovered a high mean score for compassion satisfaction, low mean score for burnout, and medium mean score for secondary traumatic stress among peer responders. These results may be due to the participants’ strong feelings of pride and recognition from their work, as well as the low number of participants that felt they had too much to do at work. Conclusion: Although peer responders derive pleasure and fulfillment from their jobs, i.e., compassion satisfaction, they also sometimes face burnout and stress due to continuous exposure to the trauma of the people they support. These results shed light on the areas that need to be targeted when creating supports for peer responders.
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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 | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".