Patient-centred care for addiction treatment: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Substance use disorders are chronic conditions that require a multidimensional treatment approach. Despite ongoing efforts to diversify such treatments, evidence continues to illuminate modest rates of treatment engagement and perceived barriers to treatment. Patient-centred care (PCC) is one approach that may strengthen the responsiveness of treatments for people with problematic substance use. The aim of this scoping review is to explore how the principles of PCC have been implemented and operationalised in healthcare settings for people with problematic substance use. METHODS AND ANALYSIS: This scoping review follows the iterative stages of the Arksey and O'Malley framework. Both empirical (from Medline, Embase, PsycINFO, CINAHL and ISI Web of Science) and grey literature references will be considered if they focused on populations with problematic substance use and described or measured PCC or one of its principles in a health-oriented context. Two reviewers will independently screen references in two successive stages of title/abstract screening and then full-text screening for references meeting title/abstract criteria. A descriptive overview, tabular and/or graphical summaries, and a directed content analysis will be carried out on extracted data. This scoping review has been registered with Open Science Framework (https://osf.io/5swvd/). ETHICS AND DISSEMINATION: This review will systematically examine the extent and nature of existing evidence of PCC in addiction research and clinical practice. Such evidence will contribute to the operationalisation of PCC for people with problematic substance use. A multidisciplinary team has been gathered to represent the needs of people with problematic substance use, healthcare providers and decision-makers. The team's knowledge users will be engaged throughout this review and will participate in dissemination activities (eg, workshops, presentations, publications, reports).
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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 | 0.116 | 0.085 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.126 | 0.026 |
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".