Quality indicators for substance use disorder care: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Substance use disorders (SUDs) are a major public health challenge, affecting millions of individuals globally and contributing to substantial morbidity and mortality. Individuals with SUDs face numerous barriers to accessing high-quality healthcare, leaving vulnerable populations susceptible to the undertreatment of SUDs. Despite the availability of clinical practice guidelines and effective interventions for SUD, there is a notable gap in the implementation and adherence to evidence-based care.Measuring the quality of care (QoC) is a critical initial step toward enhancing the treatment and services provided to individuals with SUDs. While quality indicators (QIs) for SUD care have been established in various regions, including the USA, Canada and the UK, the application of QIs for the routine measurement of QoC for SUDs is not common. Identifying and characterising the areas of low QoC in SUD management can highlight critical targets for quality improvement initiatives. However, QoC measurement in SUD care is complex, with potentially redundant indicators derived from different sources, each with its own definitions, criteria and data requirements. This scoping review aims to explore the range of QIs that are currently available to assess the QoC for individuals with SUDs. METHODS: . Reporting will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping review guidelines (PRISMA). Stage 1: the research question will be identified, clarifying the purpose of the scoping review. Stage 2: six academic databases (Cochrane Library, Embase, CINAHL, Medline, APA PsycINFO and Scopus) and grey literature sources will be searched for studies reporting QIs and published from 1990 until 2023. Stage 3: study screening and selection will be completed by two reviewers independently to review titles, abstracts and full texts based on study inclusion criteria. Stage 4: a pilot data charting form has been developed to capture information from each study, including study design, population details, setting, methodology for QI development and reported QIs. Stage 5: data synthesis and consultation will employ thematic analysis and frequency counts to categorise identified QIs within established domains for quality of healthcare. Any discrepancies in data extraction or thematic synthesis will be identified and resolved using a third reviewer when necessary. A consultation exercise using a modified Delphi process will engage experts to prioritise identified QIs, aligning with JBI recommendations for stakeholder involvement in scoping reviews. PATIENTS AND PUBLIC INVOLVEMENT: Patients and the public will not be directly involved in the design or conduct of this scoping review. However, stakeholder consultation, including individuals with lived experience of SUDs, will be incorporated during the Delphi process to prioritise identified QIs for SUD care. ETHICS AND DISSEMINATION: Ethics approval is not necessary for stages 1-4 of this scoping review as it will not involve primary data collection. Ethics approval will be obtained from the University of Calgary Health Research Ethics Board prior to the commencement of stakeholder consultation (Stage 5) in January 2025. This scoping review was preregistered on the Open Science Framework. The results of this scoping review will be disseminated through peer-reviewed publications and conference presentations. Findings will be shared with local clinicians through presentations and with the research and clinical community at relevant conferences. This study represents a necessary first step towards establishing routine QoC measurement for SUDs. Results will be used in a stakeholder consensus exercise aimed at identifying key QIs for SUD care in Alberta, Canada, that will guide the future development of continuous QoC measurement using population-based data.
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 | 0.118 | 0.104 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.085 | 0.015 |
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".