Investigating and addressing the immediate and long-term consequences of the COVID-19 pandemic on patients with substance use disorders: a scoping review and evidence map protocol
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has driven unprecedented social and economic reform in efforts to curb the impact of disease. Governments worldwide have legislated non-essential service shutdowns and adapted essential service provision in order to minimise face-to-face contact. We anticipate major consequences resulting from such policies, with marginalised populations expected to bear the greatest burden of such measures, especially those with substance use disorders (SUDs). METHODS AND ANALYSIS: We aim to conduct (1) a scoping review to summarise the available evidence evaluating the impact of the COVID-19 pandemic on patients with SUDs, and (2) an evidence map to visually plot and categorise the current available evidence evaluating the impact of COVID-19 on patients with SUDs to identify gaps in addressing high-risk populations. ETHICS AND DISSEMINATION: Ethics approval is not required for this scoping review as we plan to review publicly available data. This is part of a multistep project, whereby we intend to use the findings generated from this review in combination with data from an ongoing prospective cohort study our team is leading, encompassing over 2000 patients with SUDs receiving medication-assisted therapy in Ontario prior to and during the COVID-19 pandemic.
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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 | 0.080 | 0.117 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".