Attitudes toward opioid use disorder medications: Results from a U.S. national study of individuals who resolved a substance use problem.
Bibliographic record
Abstract
= 1,946). We examined the prevalence of positive, neutral, and negative attitudes toward agonists, such as buprenorphine/naloxone and methadone, and antagonists, such as oral and extended-release depot injection naltrexone. Single-predictor logistic regression models tested for demographic, clinical, and recovery-related correlates of these attitudes and, for those significant at the .1 level, multivariable-predictor logistic regression models tested unique associations between these correlates and attitudes. Results showed that participants were equally likely to hold positive (21.4 [18.9-24.0]%) and negative agonist (23.8 [21.2-26.7]%) attitudes but significantly more likely to hold negative (30.3 [27.4-33.3]%) than positive antagonist attitudes (18.0 [15.9-20.4]%). Neutral attitudes were most commonly endorsed for both agonists (54.8 [51.6-57.9]%) and antagonists (51.7 [48.5-54.8]%). For agonists, more recent AOD problem resolution was a unique predictor of positive attitude, whereas Black and Hispanic races/ethnicities, compared with White, were unique predictors of negative attitude. For antagonists, older age group (45-59 and 60 + vs. 18-29 years), lifetime opioid antagonist medication prescription, and past 90-day non-12-step mutual-help attendance were unique predictors of positive attitude, whereas greater spirituality was a unique predictor of negative attitude. This population-level study of U.S. adults who resolved an AOD problem showed that agonist attitudes may be more positive than anecdotal evidence suggests. Certain characteristics and experiences, however, highlight a greater likelihood of negative attitudes, suggesting these factors may be potential barriers to OUD medication adoption. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".