Personality Factors and Health Beliefs Related to Attitudes Toward Wearing Face Masks During the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract: Background: The COVID-19 virus is a worldwide pandemic health emergency. Although preventative measures have been put in place in an attempt to control its spread, their implementation has been met with resistance. Aims: To verify the nature of the health belief and personality factors associated with favorable attitudes toward face-mask use during the COVID-19 pandemic in Canada. Methods: A sample of N = 680 completed an online survey that included attitudes toward wearing face masks, measures of both malevolent (Machiavellianism, grandiose narcissism, and psychopathy) and benevolent (socially responsible) personality traits, along with a set of health beliefs surrounding COVID-19 (perceived severity and susceptibility, etc.) and the use of face masks (perceived benefits and barriers, cues to action, and self-efficacy). Results: Lower perceived susceptibility, lower perceived benefits, and higher perceived barriers to face-mask use, being more motivated by cues to action, being female, and lower levels of grandiose narcissism were uniquely related to more favorable attitudes toward wearing face masks. The relation between socially responsible personality and mask-wearing attitudes was moderated by perceived severity. Namely, higher levels of socially responsible personality predicted more favorable attitudes but only when perceived severity was also high (with the reverse being evident for those who perceived the severity as being low). Limitations: The data relies on self-reports obtained cross-sectionally from a sample of university students. Conclusions: This study sheds light on the link between pro-mask attitudes and both benevolent/malevolent personality traits and the social-contextual factors related to the enactment of preventative health behaviors.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".