Barriers to Mask Wearing for Influenza‐like Illnesses Among Urban Hispanic Households
Bibliographic record
Abstract
OBJECTIVES: To identify barriers to mask wearing and to examine the factors associated with the willingness to wear masks among households. DESIGN AND SAMPLE: We used data sources from a study assessing the impact of 3 nonpharmaceutical interventions on the rates of influenza: exit interviews; home visits with a subset of the mask group; and a focus group. MEASURES: Risk perception score, univariate analysis, and logistic regression were conducted to identify the characteristics and predictors of mask use. Thematic barriers to mask wearing were identified from qualitative data obtained at home visits and focus group. RESULTS: Respondents from the mask group, when compared with the nonmask group, demonstrated higher risk perception scores concerning influenza (maximum score: 60, means: 37.6 and 30.2, p<.001) and increased perception of effectiveness of mask wearing (maximum score: 10, means: 7.8 and 7.3, p=.043). There was no significant association between demographic, attitudinal, or knowledge variables and adherence to wearing masks. Thematic barriers were identified such as social acceptability of mask use, comfort and fit, and perception of the risk/need for masks. CONCLUSIONS: Face masks may not be an effective intervention for seasonal or pandemic influenza unless the risk perception of influenza is high. Dissemination of culturally appropriate mask use information by health authorities and providers must be emphasized when educating the public.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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 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".