Identifying behaviours for survival and wellness among people who use methamphetamine with opioids in British Columbia: A qualitative study
Bibliographic record
Abstract
Abstract Background: British Columbia has been in a state of public health emergency since 2016, due to the unprecedented numbers of overdoses and overdose deaths. Methamphetamine (MA) detection in illicit drug toxicity deaths increased from 14% in 2012 to 43% in 2020 suggesting a concerning trend of concurrent MA and opioid use in BC that reflects rising patterns identified across North America. People who use MA concurrently with opioids face an elevated risk of harm. This study aimed to identify behaviours of people who concurrently use MA and opioids practice to be safe.Methods: One-on-one semi-structured interviews were conducted by Peer Research Assistants in person and by telephone. Each interview lasted approximately 30-90 minutes and were recorded and later transcribed. Thematic analysis was carried out to identify patterns in the behaviours participants described as important to their safety in the context of concurrently using MA and opioidsResults: Participants (n=22) were distributed across the province with at least four participants from each of the five geographic health regions; 64% self-identified as men, and 50% self-identified as Indigenous. Daily MA use was reported by 72.7% of participants, and 67.3% reported using alone either often or always. From the data, we identified that participants made several considerations and adaptations in order to balance the perceived benefits and risks of their use of MA with opioids. Two overarching themes were identified to describe how participants adapted their use for survival and wellness: 1) Personal safety behaviours, and 2) interpersonal safety behaviours.Conclusions: This manuscript identified diversity in participants’ MA and opioid use (i.e. frequency, route of administration), and a subsequent range of behaviours that were performed to improve wellness and survival while using MA and opioids. Some of participants’ behaviours were practiced individually, while others relied peer support, or public health service provision. Participants identified many gaps in available services to meet their diverse needs. Harm reduction and treatment responses must be robust and adaptable to respond to the diversity of patterns of substance use among people who use opioids and MA concurrently, so as to not perpetuate harm and leave people behind.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".