Denial of prescription analgesia among people who inject drugs in a Canadian setting
Bibliographic record
Abstract
Introduction and Aims. Despite the high prevalence of pain among people who inject drugs (PWIDs), clinicians may be reluctant to prescribe opioid-based analgesia to those with a history of drug use or addiction.We sought to examine the prevalence and correlates of PWIDs reporting being denied of prescription analgesia (PA).We also explored reported reasons for and actions taken after being denied PA. Design and Methods. Using data from two prospective cohort studies of PWIDs, multivariate logistic regression was used to identify the prevalence and correlates of reporting being denied PA. Descriptive statistics were used to characterise reasons for denials and subsequent actions. Results. Approximately two-thirds (66.5%) of our sample of 462 active PWIDs reported having ever been denied PA.We found that reporting being denied PA was significantly and positively associated with having ever been enrolled in methadone maintenance treatment (adjusted odds ratio 1.76, 95% confidence interval 1.11–2.80) and daily cocaine injection (adjusted odds ratio 2.38, 95% confidence interval 1.00–5.66). The most commonly reported reason for being denied PA was being accused of drug seeking (44.0%). Commonly reported actions takenafter being denied PA included buying the requested medication off the street (40.1%) or obtaining heroin to treat pain(32.9%). Discussion and Conclusions. These findings highlight the challenges of addressing perceived pain and the need for strategies to prevent high-risk methods of self-managing pain, such as obtaining diverted medications or illicit substances for pain. Such strategies may include integrated pain management guidelines within methadone maintenance treatment and other substance use treatment programs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".