The Shadow Pandemic of Alcohol Use during COVID-19: A Canadian Health Leadership Imperative
Bibliographic record
Abstract
Increased alcohol consumption among Canadians during the COVID-19 pandemic will impact our health systems in the short and longer term, through increased hospitalizations due to alcohol-related illness, addiction, violence and accidents.The increased stress due to involuntary unemployment, confinement and boredom during the pandemic has led to an escalation in alcohol use.It is imperative that policy makers recognize and address the inherently conflicting roles of provincial/territorial governments as regulators/retailers of alcohol and funders of healthcare and prioritize the development and implementation of an evidencebased framework to mitigate the increased population health risks of alcohol-related harms. RésuméL' augmentation de la consommation d' alcool chez les Canadiens pendant la pandémie de COVID-19 aura un impact sur les systèmes de santé à court et à long terme; impact résultant d' une augmentation des hospitalisations en raison de maladies liées à l' alcool ainsi que de problèmes de toxicomanie, de violence et d' accidents.Le stress accru dû au chômage involontaire, au confinement et à l' ennui pendant la pandémie a conduit à une escalade de The Shadow Pandemic of Alcohol Use during COVID-19: A Canadian Health Leadership ImperativeLa pandémie cachée de la consommation d' alcool pendant la COVID-19 : un impératif en matière de leadership en santé au Canada
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.014 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".