Longitudinal trends in service utilisation of Alcohol and Other Drugs Services (AODS) during COVID-19: A Scoping Review
Bibliographic record
Abstract
Background: COVID-19 has affected service delivery and utilization of AODS worldwide with a potential negative impact on service users and staff. Objectives: To understand the trends of service utilization of AODS, identify knowledge gaps, and provide directions for future research and planning. Methods: Medline, Embase, CINAHL, PubMed and PsycINFO were searched for original articles published in English since 2019, with quantitative analysis of service utilization of AODS. Of the 1546 initial search results, 938 were screened after de-duplication and 43 underwent full-text review. Data extracted from 30 studies informed this review. Results: Of the studies, 29 were from high-income countries and 15 focused on medication for opioid use disorders (MOUD). An initial reduction of service utilization followed by gradual improvement was seen in most treatment types, with most disruptions in residential programs, outreach services, home visits, group therapy and needle syringe programs and fewer disruptions in individual counselling and MOUD. Treatment initiations decreased, while treatment adherence improved during COVID-19. The use of telehealth and treatment-related policy changes were associated with improved service utilization. Telehealth was associated with catchment expansion and broadening of service-user profiles. An increased tendency to use the opioid long-acting injection was reported in Australia. Conclusions: The impact of COVID-19 on AODS changed over time and according to, the drug/treatment type and geographical remoteness. Main contributors to minimizing disruptions in service utilization included treatment-related policy changes, telehealth, and newer treatment modalities. Longitudinal studies beyond 2021 and studies on regional/rural AODS and AOD workforce are recommended.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.020 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".