Experiences of healthcare and substance use treatment provider-based stigma among patients receiving methadone
Bibliographic record
Abstract
Medications for Opioid Use Disorder (MOUD) are efficacious, however only one-third of individuals with an opioid use disorder (OUD) enter into treatment. Low rates of MOUD utilization are partially due to stigma. This study examines provider-based stigma toward MOUD and identifies factors associated with experiencing stigma related to MOUD from substance use treatment and healthcare providers among people receiving methadone. Clients receiving MOUD at an opioid treatment program (N = 247) were recruited to complete a cross-sectional computer-based survey assessing socio-demographics, substance use, depression and anxiety symptoms, self-stigma, and recovery supports/barriers. Logistic regression was used to examine factors associated with hearing negative comments about MOUD from substance use treatment and healthcare providers. 27.9% and 56.7% of respondents reported they sometimes/often hear negative comments about MOUD from substance use treatment and healthcare providers, respectively. Logistic regression results indicate that individuals who experience more negative consequences resulting from their OUD (OR=1.09, p=.019) had greater odds of hearing negative comments from substance use treatment providers. Age (OR=0.966, p=.017) and treatment stigma (OR=1.42, p=.030) were associated with greater odds of hearing negative comments from healthcare providers. Stigma can be a deterrent to seeking substance use treatment, healthcare, and recovery support. Understanding factors associated with experiencing stigma from substance use treatment providers and healthcare providers is important as these individuals may act as advocates for those with OUD. This study highlights individual factors associated with hearing negative comments about methadone and other MOUD and point to areas for targeted education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".