Association Between Depression, Health Beliefs, and Face Mask Use During the COVID-19 Pandemic
Bibliographic record
Abstract
The 2019 novel coronavirus (COVID-19) pandemic is associated with increases in psychiatric morbidity. It is unclear if similar rates are evident outside of mainland China or if people with depressive symptoms understand or apply COVID-19 information and face mask use guidelines differently to the general population. Therefore, this study aimed to examine associations between depression, health beliefs and face mask use during the COVID-19 pandemic among the general population in Hong Kong. This study gathered data from 11,072 Hong Kong adults via an online survey. Respondents self-reported their demographic characteristics, depressive symptoms (PHQ-9), face mask use, and health beliefs about COVID-19. Hierarchical logistic regression was used to identify independent variables associated with depression. The point-prevalence of probable depression was 46.5% (n=5,150. Respondents reporting higher mask reuse (OR=1.24, 95%CI 1.17-1.34), wearing masks for self-protection (OR=1.03 95%CI 1.01-1.06), perceived high susceptibility (OR=1.15, 95%CI 1.09-1.23) and high severity (OR=1.33, 95%CI 1.28-1.37) were more likely to report depression. Depression was less likely in those with higher scores for cues to action (OR=0.82, 95%CI 0.80-0.84), knowledge of COVID-19 (OR=0.95, 95%CI 0.91-0.99) and self-efficacy to wear mask properly (OR=0.90 95%CI 0.83-0.98). We identified a high point-prevalence of probable major depression and suicidal ideation during the COVID-19 outbreak in Hong Kong. The findings highlight that COVID-19 health information may be a protective factor of probable depression and suicidal ideation during the pandemic. Accurate and up-to-date health information should be disseminated to vulnerable subpopulations, perhaps using digital health technology and social media platforms to prompt professional help-seeking behaviour.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".