The “Fear Factor” for Surgical Masks and Face Shields, as Perceived by Children and Their Parents
Bibliographic record
Abstract
OBJECTIVE: The goal was to determine whether young children and their parents prefer physicians wearing clear face shields or surgical masks. METHODS: Eighty children (4-10 years of age) and their guardians were recruited from a pediatric emergency department. A survey and color photographs of the same male and female physicians wearing face shields and surgical masks were distributed. The parents were asked to decide which set of physicians they would prefer to care for their children and with which set of physicians they thought their children would be most comfortable. The children then were asked to decide which set of physicians they would prefer to take care of them and why. The children also were asked whether they found any of the physicians frightening and, if so, why. RESULTS: Fifty-one percent of parents preferred the pictures of physicians wearing face shields, and 62% thought that their children would choose the physicians in the face shields because their faces were visible and therefore less frightening. However, 59% of children stated that either set of physicians would be fine and neither was frightening; if given a choice, 49% would choose physicians in face shields. CONCLUSIONS: Physicians and parents have a perception that surgical masks are frightening to all children. Our study has shown that this perception is not completely true. Face shields may be a better choice, however, because both parents and children would prefer this option.
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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 | 0.008 |
| 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 | 0.001 |
| 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".