Negative attitude towards wearing facemask during COVID-19 pandemic: A survey based on educational settings
Bibliographic record
Abstract
Objectives of the Study: In recent studies, it was suggested that wearing facemask in public places is an effective way to break the chain of spreading coronavirus (COVID-19). Early in the pandemic, some studies evident that wearing mask is not compulsory for healthy individuals and it reserved for health workers and the person infected with COVID-19 only. These types of statement create confound into public and increase the prevalence of wearing facemask. Therefore, the present survey study has been designed to investigate the attitude of students towards wearing facemask during COVID-19 pandemic.Design of the Study: A Cross-sectional study design was adopted for the present studyMaterial and Methods: The sample consist of 1202 subjects from the Northern India, Haryana and Delhi NCR regions. The mean age of the subjects were 21.29 years (SD = 7.07, Range = 8 – 58 years) and 95% confidence interval for age was 21.29±0.40 margin of error for upper and lower bound. Data were collected between 15 March, 2021 to 12 April 2021 in general public settings (N=1206) using ‘Google Form’ an internet based self-report survey tool. Respondents completed a 12 items scale developed for assessing negative attitude toward wearing facemask. The present scale was derived from the study conducted by ‘Steven Taylor’ [1, 2] in United States and Canada. Independent ‘t’ test and one way analysis of variance followed by Pot-hoc method was used for mean score of different groups. Level of significance was set at 0.05 respectively.Results: A low prevalence towards wearing facemask observed among participant. A significant difference was taking into noticed between male and female participant in respect of wearing facemask. Male participant revealed high negative attitude towards wearing facemask. Similar outcomes observed in rural participants as male participants regarding practice of facemask. In respect of different age categories high negative attitude found in 8 to 18 years age group which indicate a higher disobedience among children and adolescence towards public health recommendations.Conclusions: In conclusion, in the face of pandemic where, wearing facemask is a key preventive measure to break the chain of spreading the virus among masses. Based on obtained outcomes of the present study, it was recommended that govt. of India should take necessary implementation to improve the awareness of using facemask in public.
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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) | 0.001 | 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 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".