Service-level barriers to and facilitators of access to services for the treatment of alcohol use disorder and problematic alcohol use: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: Prior to the COVID-19 pandemic, substance use health services for treatment of alcohol use disorder and problematic alcohol use (AUD/PAU) were fragmented and challenging to access. The pandemic magnified system weaknesses, often resulting in disruptions of treatment as alcohol use during the pandemic rose. When treatment services were available, utilisation was often low for various reasons. Virtual care was implemented to offset the drop in in-person care, however accessibility was not universal. Identification of the characteristics of treatment services for AUD/PAU that impact accessibility, as perceived by the individuals accessing or providing the services, will provide insights to enable improved access. We will perform a scoping review that will identify characteristics of services for treatment of AUD/PAU that have been identified as barriers to or facilitators of service access from the perspectives of these groups. METHODS AND ANALYSIS: We will follow scoping review methodological guidance from the Joanna Briggs Institute. Using the OVID platform, we will search Ovid MEDLINE including Epub Ahead of Print and In-Process and Other Non-Indexed Citations, Embase Classic+Embase, APA PsychInfo, Cochrane Register of Controlled Trials, the Cochrane Database of Systematic Reviews and CINAHL (Ebsco Platform). Multiple reviewers will screen citations. We will seek studies reporting data collected from individuals with AUD/PAU or providers of treatment for AUD/PAU on service-level factors affecting access to care. We will map barriers to and facilitators of access to AUD/PAU treatment services identified in the relevant studies, stratified by service type and key measures of inequity across service users. ETHICS AND DISSEMINATION: This research will enhance awareness of existing evidence regarding barriers to and facilitators of access to services for the treatment of alcohol use disorder and problematic alcohol use. Findings will be disseminated through publications, conference presentations and a stakeholder meeting. As this is a scoping review of published literature, no ethics approval was required.
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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 | 0.096 | 0.092 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.017 | 0.024 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.080 | 0.014 |
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".