A Discrete-Choice Experiment to Assess Patient Preferences for Long-Acting Injectable Treatments in Opioid Use Disorder in Australia, Finland, Germany, and Italy
Bibliographic record
Abstract
Abstract Opioid use disorder (OUD) is characterized by dependence on opioids along with an inability to manage their use, leading to behaviors that impact patients’ lives. Long-acting injectable (LAI) treatments for OUD offer benefits compared with short-acting options. This study assessed treatment preferences of patients with OUD in Australia, Finland, Germany, and Italy regarding attributes of LAI buprenorphine. Based on a literature review and consultations, six attributes of OUD treatments were included in a discrete choice experiment study, conducted online. Participants who completed the survey ( n = 317) were aged 37.2 ± 12 years, with 54% male and 36% having prior LAI experience. Higher preferences were expressed for less frequent injections, higher chance of staying off unprescribed opioids after six months of treatment, treatment through general practitioners or specialized centers, reduced withdrawal symptoms, earlier onset of treatment benefits, and a shorter timeframe to feel stable. Patients valued independence, fewer clinic visits, interpersonal relationships, and achieving sobriety. In summary, patients preferred LAI treatments with improved profiles on frequency, withdrawals, onset, and opioid abstinence, along with reduced stigma, fewer visits, and sustained benefits.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".