Denial of Pain Medication by Health Care Providers Predicts In‐Hospital Illicit Drug Use among Individuals who Use Illicit Drugs
Bibliographic record
Abstract
BACKGROUND: Undertreated pain is common among people who use illicit drugs (PWUD), and can often reflect the reluctance of health care providers to provide pain medication to individuals with substance use disorders. OBJECTIVE: To investigate the relationship between having ever been denied pain medication by a health care provider and having ever reported using illicit drugs in hospital. METHODS: Data were derived from participants enrolled in two Canadian prospective cohort studies between December 2012 and May 2013. Using bivariable and multivariable logistic regression analyses, the relationship between having ever been denied pain medication by a health care provider and having ever reported using illicit drugs in hospital was examined. RESULTS: Among 1053 PWUD who had experienced ≥ 1 hospitalization, 452 (44%) reported having ever used illicit drugs while in hospital and 491(48%) reported having ever been denied pain medication. In a multivariable model adjusted for confounders, having been denied pain medication was positively associated with having used illicit drugs in hospital (adjusted OR 1.46 [95% CI 1.14 to 1.88]). CONCLUSIONS: The results of the present study suggest that the denial of pain medication is associated with the use of illicit drugs while hospitalized. These findings raise questions about how to appropriately manage addiction and pain among PWUD and indicate the potential role that harm reduction programs may play in hospital settings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".