THE INCIDENCE OF ALCOHOL USE DISORDERS DURING THE COVID-19 PANDEMIC
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has introduced a myriad of challenges to healthcare systems and public health policies across the globe. Individuals with alcohol use disorders are at peaked risk due to mental, socio-demographic, and economic factors leading to hindered mental health service access, misinformation and adherence. METHODS: Keywords including "alcohol use", "death", "hand sanitizer", "overdose" and "COVID-19" were used to obtain 8 media reports for case analysis. A review of 34 manually extracted records were also conducted using PubMed, MEDLINE, Scopus, and the Embase database with no time and language restrictions. RESULTS: A total of 2,517 individuals with alcohol overdose across the United States, India, Canada, and Iran were presented. The majority of cases were male, ages 21-65. Common contributors were linked to socio-economic changes, disruption to mental health services, and physical isolation. CONCLUSION: While original studies are essential to evaluate the etiologies of alcohol use and misuse during pandemics, the dissemination of misinformation must be curbed by directing vulnerable individuals towards accurate information and access to mental health services.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".