The Fear of COVID-19, Demographic Factors, and Substance use in a Multinational Sample Amid the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract The global pandemic caused by the coronavirus disease 2019 (COVID-19) had mental health consequences such as fear. Scholars have argued that when people are fearful, they may use substances to escape from fear, and demographic variables can have implications on how to target interventions to people. To date, little is known about how the fear of COVID-19 and demographic factors may contribute to substance use amid the COVID-19 pandemic. From 3 June to 10 June 2020, a cross-sectional study was conducted with 202 residents (Mean age = 41.77 ± 11.85; age range = 18-70 years) in 14 countries. A standardized questionnaire was utilized for data collection, SPSS (version 22.0) was utilized for data analysis, and p < .05 implied statistical significance. Descriptive statistics revealed that residents in Canada scored the highest mean score in the fear of COVID-19 scale, while residents in Australia scored highest in the substance use scale. Further, fear of COVID-19 had a negative nonsignificant relationship with substance use (r = −.07; df = 200; p > .05). Males (Mean = 18.21) scored significantly higher than females (Mean = 14.06) in substance use [t (200) = 1.9; p < .05]. The younger age group (18-28 years) scored the highest mean score in substance use compared to older age groups (29-39 years, 40-50 years, 51-61 years, and 62-72 years); however, it was not significant [F (4, 197) = 2.04; p > .05]. These data contribute to informing future studies that add more questions regarding how different variables may contribute to substance use during subsequent waves of 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".