Substance use and overdose risk: documenting the perspectives of formerly incarcerated persons in the Fraser East region of BC
Bibliographic record
Abstract
BACKGROUND: The relationship between incarceration and risk of overdose has been well-documented in the literature, but few studies document the perspectives of persons at risk of overdose who were incarcerated. This sub-inquiry aimed to understand the experiences of persons with a history of substance use and incarceration in the Fraser East region of BC and how involvement with the criminal justice system affected their drug use and perceived risk of overdose. METHODS: The Fraser East Overdose Response project utilized a community-based participatory action approach that included peer researchers with lived experience in all parts of the research process. This qualitative pilot study aimed to better understand individuals at risk of an unwitnessed overdose in order to prevent deaths and identify effective local responses. A snowball sampling technique was used to recruit persons aged 19 and over who used illicit drugs over the past 3 years in the Fraser East since 2016. In total, 22 participants were interviewed. Of these, 13 participants identified a history of incarceration. Interviews were analyzed using a framework analysis approach. RESULTS: The perspectives that participants shared revealed that impacts from incarceration are influenced by policies but also highly individualized. Our inquiry found three broader themes, within which were situated differing and sometimes conflicting interpretations and experiences of systemic environments: (1) incarceration was associated with harms and was perceived to increase risk of overdose following release, (2) incarceration was perceived to have limited impact on substance use and overdose risk, and (3) incarceration was associated with a perceived reduction of substance use and overdose risk. CONCLUSIONS: Understanding the complexities of the perceptions of those with lived experience of substance use and incarceration is of importance to better inform interventions in this population. The existing knowledge base urgently requires further inquiry into the intersections between qualitative perspectives, environments and policies, and quantitative outcomes of overdose vís-a-vís correctional institution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".