Misuse of Prescription Opioid Medication among Women: A Scoping Review
Bibliographic record
Abstract
Background. National data from Canada and the United States identify women to be at greater risk than men for the misuse of prescription opioid medications. Various sex- and gender-based factors and patient and physician practices may affect women's use and misuse of prescription opioid drugs. Objectives. To explore the particular risks, issues, and treatment considerations for prescription opioid misuse among women who experience chronic noncancer pain and trauma. Methods. A scoping review for articles published between January 1990 and May 2014 was conducted on sex- and gender-based risks and treatment considerations among women who experience chronic noncancer pain and trauma. Results. A total of 57 articles were identified. The present narrative review summarizes the specific risks for the misuse of prescription opioid medication among women who have experienced violence and trauma, Aboriginal women, adolescents and young women, older women, pregnant women, women of a sexual minority, and transwomen. Discussion. The majority of the literature is descriptive, with few studies that evaluate approaches and interventions to respond to the issue of chronic pain, trauma, and misuse of prescription opioids among women, particularly vulnerable subgroups of women. Conclusions. Trauma-informed and women-centred approaches that address women's vulnerabilities and complex needs require further attention.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".