Patient engagement, treatment preferences and shared decision-making in the treatment of opioid use disorder in adults: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Opioid use disorder (OUD) is characterised by the fifth Edition of the Diagnostic and Statistics Manual as a problematic pattern of opioid use (eg, fentanyl, heroin, oxycodone) that leads to clinically significant impairment. OUD diagnoses have risen substantially over the last decade, and treatment services have struggled to meet the demand. Evidence suggests when patients with chronic illnesses are matched with their treatment preferences and engaged in shared decision-making (SDM), health outcomes may improve. However, it is not known whether SDM could impact outcomes in specific substance use disorders such as OUD. METHODS AND ANALYSIS: . The search strategy was developed to retrieve relevant publications from database inception and June 2017. MEDLINE, EMBASE, PsycINFO, Cochrane Database for Controlled Trials, Cochrane Database for Systematic Reviews and reference lists of relevant articles and Google Scholar will be searched. Included studies must be composed of adults with a diagnosis of OUD, and investigate SDM or its constituent components. Experimental, quasi-experimental, qualitative, case-control, cohort studies and cross-sectional surveys will be included. Articles will be screened for final eligibility according to title and abstract, and then by full text. Two independent reviewers will screen excluded articles at each stage. A consultation phase with expert clinicians and policy-makers will be added to set the scope of the work, refine research questions, review the search strategy and identify additional relevant literature. Results will summarise whether SDM impacts health and patient-centred outcomes in OUD. ETHICS AND DISSEMINATION: Scoping review methodology is considered secondary analysis and does not require ethics approval. The final review will be submitted to a peer-reviewed journal, disseminated at relevant academic conferences and will be shared with policy-makers, patients and clinicians.
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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 | 0.066 | 0.054 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.016 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.061 | 0.008 |
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".