Медицинская маска как средство индивидуальной и коллективной защиты в условиях пандемии COVID-19 (кросс-культурные аспекты)
Bibliographic record
Abstract
Using a medical mask is one of the ways to prevent the spread of respiratory infections, including COVID-19. The willingness to use the mask in different countries depends on a number of factors – epidemiological experience, cultural norms and attitudes, economic and political conditions, public awareness, etc. The purpose of this study is to analyze the first reaction of the population to prescriptions or recommendations of authorities to use medical masks as a means of personal protection during COVID-19 in different countries. We used methods of semi-structured in-depth interviews, questionnaires, photo-fixation of elements of people's behavior during the pandemic, content analysis of the media. In total, 366 questionnaires were collected (51 males and 315 females) among Russian-speaking people who were living or staying abroad at the time of the outbreak and spreading of COVID-19, as well as among Russians from different regions of Russia since March 23 to April 10, 2020. An analysis of the data showed that the willingness to use a mask depends on the country of residence of the respondent, gender, social distance and trust in the authorities. The results of the study demonstrate the effect of official decisions on the real behavior of people – the more serious are measures on the part of the government, the more responsible are people about preventive measures and the better they implement them. The results were interpreted from the point of view of “Individualism-Collectivism” dichotomous classification of cultures. These results are the first step of the study and reveal the first stage of people's response to the restrictions. Understanding of socio-cultural and ethnic circumstances of population’s perception and implementation of sanitary-hygienic prescriptions can be used in choosing the most optimal ways of warning and developing efficient methods to encourage the population to adhere to restrictions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".