Normalizing fentanyl: interpreting the perceived ‘risk’ of correctional officer work
Bibliographic record
Abstract
BACKGROUND: Scholarship on how fentanyl affects the complexities of correctional settings is limited in Canada, as scholars have focused on the prevalence of opioid use and overdose in prisons, as well as community treatment and access following release. Fentanyl constitutes a continuing challenge both in prisons and broader society. RESULTS: The current qualitative, interview-based empirical study examines how fentanyl is interpreted by correctional officers (COs, n = 99) across federal prisons in Canada, some of whom have worked in institutions with a high presence of fentanyl, while others have less exposure to the drug. We found that while many COs had responded to an overdose during their first or second year on the job, most COs who had did not perceive the event to be psychologically traumatic nor were concerned about the presence and availability fentanyl in their work environment, or they were indifferent. Yet this finding competes with the 41.4% of officers who did express concern about the presence of fentanyl - suggesting both a "normalization" of fentanyl as a workplace hazard as well as an underpinning social concern. CONCLUSIONS: We discuss the implications of these complicated findings in relation to reducing workplace stressors and countering misinformation that, in addition to other potential occupational factors, may be responsible for the concerns of COs tied to the presence of fentanyl.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".