Digital Interventions to Save Lives From the Opioid Crisis Prior and During the SARS COVID-19 Pandemic: A Scoping Review of Australian and Canadian Experiences
Bibliographic record
Abstract
Background: The potential for digital initiatives for opioid harm reduction is boundless. Synthesized evidence on current interventions and their efficacy are emerging. This scoping review is an effort to aggregate Canadian and Australian digital health initiatives used to prevent opioid-related deaths and minimize harm, prior to and particularly during the pandemic of SARs-COVID-19, when the crisis escalated. Methods: The Joanna Briggs Institute's methodological framework for conducting scoping reviews was used. Peer reviewed and gray literature published between January 2016 to October 2021 were included. Search translation was performed across CINAHL, Cochrane, SCOPUS, MEDLINE Complete, and ProQuest Public Health with consistent use of key search terms. Citation checks were also conducted. Studies included were written in English and reported on digital technologies to prevent opioid-related harm and/or mortality in participants aged 18 years or older in Australia and Canada. Results: = 3). Telehealth-delivered opioid replacement therapy demonstrated equal outcomes and treatment retention rates compared to in-person and mobile applications for overdose monitoring demonstrated lifesaving capability through direct linkages with emergency response services. Conclusions: Digital interventions to minimize opioid crisis related harm and overdose prevention are fast emerging in Australia and Canada. During the pandemic, the crisis escalated in both countries as a public health emergency, and different initiatives were trialed. Digital harm reduction solutions via mobile apps (or SaaS solutions) were found to have the potential to prevent accidental overdose deaths and save lives, if rendered through privacy preserved, secure and trust enabled methods that empower users. Knowledge sharing between the two countries, relating to suitable interventions, may add significant value in combatting the escalating opioid crisis in the post pandemic era.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".