Parent-for-child mask behavior during the COVID-19 pandemic in Canada and the United States: An investigation of attitudes, norms, and perceived control using the theory of planned behavior
Bibliographic record
Abstract
Face masks continue to be a necessity until a large proportion of the population, including children, receive immunizations for COVID-19. The aim of this study was to investigate the relationship between parental attitudes and beliefs about masks and parent-for-child mask behavior using the Theory of Planned Behavior. We administered a survey in August 2020 to parents of school-aged children residing in the United States and Canada. Measures included sociodemographic variables for the parent and child, attitudes, norms, perceived control over children’s mask use, intentions and enforcement of mask wearing among children (also titled “parent-for-child mask behavior”). Data were analyzed using structural equation modelling. We collected data from 866 parents and 43.5% had children with pre-existing conditions (e.g., allergies, anxiety, impulsivity, skin sensitivity, asthma) that made extended mask wearing difficult, as per parent’s report. Among the full sample, negative attitudes (β = -0.20, p = .006), norms (β = 0.41, p = .002), and perceived control (β = 0.33, p = .006) predicted intentions. Norms (β = 0.50, p = .004) and intentions (β = 0.28, p = .003) also predicted parent-for-child mask use, while attitudes and perceived control did not. Intentions mediated the associations between attitudes, norms, perceived control, respectively, and mask behavior. Subgroup analyses revealed intentions as the key predictor of parent-for-child mask use among children with pre-conditions and norms as the key predictor among children without pre-conditions (i.e. healthy). Future public health messaging should target parental intentions, attitudes, norms, and perceived control about children’s masks wearing.
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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) | 0.000 | 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 | 0.000 |
| 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".