Quality of life and relapse of Opioid Use Disorder: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Quality of life (QoL) greatly influences the outcomes of patients with mental illnesses and there is evidence that there is an association between QoL and the relapse of Opioid Use Disorder (OUD). However, no reviews elucidate the relationship between QoL and the relapse of OUD. This document provides a scoping review protocol that aims to systematically chart and synthesise the published, unpublished and grey literature about the relationship between QoL and relapse of OUD. METHODS AND ANALYSIS: The enhanced six-stage methodological framework for scoping reviews of Arksey and O'Malley will be used. The main research question guiding the review will be: What is the relationship between QoL and relapse of OUD? Peer-reviewed and non-peer-reviewed articles, reports, and policy documents will be eligible to be included in the review with no limits on publication date. PubMed, PsycINFO, Google Scholar, Scopus, OVID and Cochrane Library will be among the databases searched. We shall identify grey literature from Google Scholar, ProQuest database, Grey Source Index, Open Grey and OpenDOAR. The reporting of the review will follow the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. Criteria for evidence inclusion and exclusion will be used during literature screening and mapping. ETHICS AND DISSEMINATION: Patients and the public will not be involved in the interpretations of the findings, therefore, we shall not seek approval from an ethics committee. Results will be disseminated through publication in a peer-reviewed, scientific journal, conference presentations.
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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 | 0.132 | 0.097 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.101 | 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".