Pain as a risk factor for substance use: a qualitative study of people who use drugs in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: People who use drugs have a significantly higher prevalence of chronic non-cancer pain compared to the general population, yet little is known about how various policy, economic, physical, and social environments may serve as risk or protective factors in the context of concurrent pain and substance use. Therefore, this study sought to explore perspectives, risks, and harms associated with pain among people who use drugs. METHODS: Thirteen focus group interviews were held across British Columbia, Canada, from July to September 2015. In total, 83 people who had lived experience with substance use participated in the study. Using an interpretive description approach, themes were conceptualized according to the Rhodes' Risk Environment and patient-centered care frameworks. RESULTS: Participants described how their experiences with inadequately managed pain in various policy, economic, physical, and social environments reinforced marginalization, such as restrictive policies, economic vulnerability, lack of access to socio-physical support systems, stigma from health professionals, and denial of pain medication leading to risky self-medication. Principles of patient-centered care were often not upheld, from a lack of recognition of patients as experts in understanding their unique pain needs and experiences, to an absence of shared power and decision-making, which often resulted in distrust of the patient-provider relationship. CONCLUSIONS: Various risk environments and non-patient-centered interactions may contribute to an array of health and social harms in the context of inadequately managed pain among people who use drugs.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| 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".