Factors Associated with Perceived Abuse in the Health Care System Among Long-Term Opioid Users: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Opioid-dependence is a chronic relapsing disorder. Histories of physical, sexual, and emotional abuse are prevalent among long-term opioid users. While perceived abuse in health care has been linked to histories of abuse in other populations it has not been investigated among long-term opioid users. OBJECTIVE: To determine factors associated with perceived abuse in health care among a sample of long-term opioid users. METHODS: Gender Matters in the Health of Long-Term Opioid Users (GeMa) was a descriptive cross-sectional study. Participants (n = 175) answered questions on health, drug use, treatment history, and victimization. A multivariable model of perceived abuse in health care was built using logistic regression. RESULTS: Half of participants (n = 88) reported perceived abuse in health care in lifetime with no gender differences. Histories of abuse, physical, and psychological health problems, and health care access were more prevalent among those reporting perceived abuse in health care compared to those not reporting such experiences. Multivariable analysis showed that more methadone maintenance treatment attempts in life, prescribed psychiatric medication in life, and having higher childhood emotional abuse scores were independently associated with perceived abuse in health care. Among all childhood neglect and abuse types measured, emotional abuse was the only significant predictor. CONCLUSIONS: A high prevalence of lifetime perceived abuse in health care (50%) was reported, along with extremely high childhood abuse and neglect scores. Consideration of these variables by health care and service providers is extremely important to improving patient perceptions of care, and ultimately health and treatment outcomes among opioid-dependent people.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".