Shared decision-making and client-reported dose satisfaction in a longitudinal cohort receiving injectable opioid agonist treatment (iOAT)
Bibliographic record
Abstract
BACKGROUND: Across different types of oral Opioid Agonist Treatment for people with Opioid Use Disorder, receiving a dose that meets their needs is associated with better outcomes. Evidence also shows patients are more likely to receive an "adequate dose" when their prescribers are involving them in decision making. Neither of these findings have been studied in the context of injectable Opioid Agonist Treatment, which is the purpose of this study. METHODS: This study was a retrospective analysis of an 18-month prospective longitudinal cohort study of 131 people receiving injectable Opioid Agonist Treatment. In the 18-month study, observations were collected every two months for one year, and then once more at 18 months. At 6 months, participants were asked whether their dose was satisfactory to them (outcome variable). Generalized Estimating Equations were used, to account for multiple observations from each participant. The final multivariate model was built using a stepwise approach. RESULTS: Five hundred forty-five participant-observations were included in the analysis. Participant-observations were grouped by "dose is satisfactory" and "wants higher dose". From unadjusted analyses, participants were less likely to report being satisfied with their dose if they: were Indigenous, had worse psychological or physical health problems, had ever attempted suicide, were younger when they first injected any drug, were a current smoker, felt troubled by drug problems, gave their medication a lower "drug liking" score, and felt that their doctor was not including them in decisions the way they wanted to be. In the final multivariate model, all previously significant associations except for "current smoker" and "troubled by drug problems" were no longer significant after the addition of the "drug liking" score. CONCLUSIONS: Patients in injectable Opioid Agonist Treatment who are not satisfied with their dose are more likely to: be troubled by drug problems, be a current smoker, and report liking their medication less than dose-satisfied patients. Prescribers' practicing shared decision-making can help patients achieve dose-satisfaction and possibly alleviate troubles from drug problems. Additionally, receiving a satisfactory dose may be dependent on patients being able to access an opioid agonist medication (and formulation) that they like.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".