Recovery Goals and Long-term Treatment Preference in Persons Who Engage in Nonmedical Opioid Use
Bibliographic record
Abstract
BACKGROUND: While most opioid use disorder (OUD) treatment providers consider opioid abstinence to be the preferred outcome, little is known about the treatment preferences of the larger population of individuals who engage in nonmedical opioid use and have not yet sought treatment. This study sought to descriptively quantify the proportion of out-of-treatment individuals with nonmedical opioid use that have abstinent and nonabstinent recovery goals. METHODS: Participants (N = 235) who engage in nonmedical opioid use and met self-reported criteria for OUD were recruited online and participated in a cross-sectional survey on recovery goals and treatment perceptions. Participants were dichotomized as having either abstinent (70.6%) or nonabstinent (29.4%) recovery goals. Participants were presented with 13 treatment options and asked which treatment they would "try first" and which treatment they thought would be the best option for long-term recovery. RESULTS: Persons in the nonabstinent group were more likely to want to continue use of prescription opioids as prescribed by a physician compared with the abstinent group (χ[1] = 9.71, P = 0.002). There were no group differences regarding preference for individual OUD treatments. The most frequently endorsed treatments that participants would "try first" were physician visits (23.4%), one-on-one counseling (18.7%), and 12-step groups (13.2%), whereas the most frequently endorsed treatments for long-term recovery were one-on-one counseling (17.4%), residential treatment (16.7%), and buprenorphine (15.3%). CONCLUSION: Public health initiatives to engage out-of-treatment individuals should take into account recovery goals and treatment preferences to maximize treatment initiation and retention.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".