Harm Reduction, Stigma and the Problem of Low Compassion Satisfaction
Bibliographic record
Abstract
Background and Objective Canada is in the midst of an opioid crisis. Given the sheer magnitude of the crisis and escalating death toll, the mobilization of harm reduction interventions is an important priority. Currently, little is known about the role played by stigmatization, particularly in terms of how this may impact the endorsement and uptake of harm reduction strategies and initiatives among frontline providers. Materials and Methods Opening Minds, the anti-stigma initiative of the Mental Health Commission of Canada, undertook a one-and-a-half-year research project to understand the qualities, characteristics, sources, consequences, and solutions to the problem of stigmatization on the front-lines of the opioid crisis. A qualitative key informant design was selected. Participants included various first responder and health provider groups, people with lived experience of opioid or other drug use, and people in key policy or programming roles. Eight focus groups were held across Canada, and 15 one-on-one key informant interviews were completed. Results Analysis of focus group and key informant interviews revealed three main ways in which stigma shows up on the front lines of the opioid crisis among providers. These themes coalesced around a central main problem, that of low compassion satisfaction. Suggestions for how these concerns can be addressed were also identified. Conclusion The findings from this research revealed several key ways that stigma shows up in the experiences and perceptions of frontline providers and provide several promising avenues for combating stigmatization related to opioid use and harm reduction. An important avenue for future research is to develop and elaborate on the theoretical connections between the concepts of stigmatization and compassion satisfaction as a way to better understand the problem of stigmatization in helping environments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".