Face Mask Exemptions, Respiratory Patients, and COVID-19 in Spain. Data From the 2021 ITC EUREST-PLUS Spain Survey
Bibliographic record
Abstract
y COVID-19 en Espa ña. Datos de la Encuesta ITC EUREST-PLUS Espa ña 2021Dear Editor, During the initial waves of the COVID-19 pandemic, respiratory patients received confusing messages by several patient associations and the World Health Organization 1,2 to be exempted from wearing face masks.Allegedly, many assumed that difficult breathing through face masks might exacerbate their respiratory condition, producing asthma attacks or chronic obstructive pulmonary disease (COPD) exacerbations.In its December 2020 Interim Guidance on Mask use in the context of COVID-19, WHO cited studies suggesting that masks might have an adverse impact on respiratory disease patients, but they did not provide any guidance about whether respiratory disease patients should or should not wear masks; explicitly, it reads in page 10 of 22 the following paragraph and references: ". . .mask use may have disadvantages for or difficulty wearing masks, especially for children, developmentally challenged persons, those with mental illness, persons with cognitive impairment, those with asthma or chronic respiratory or breathing problems, those who have had facial trauma or recent oral maxillofacial surgery and those living in hot and humid environments".3,4 Indeed, the Spanish Society of Pneumology and Thoracic Surgery (SEPAR) issued a statement to the Ministry of Health in October 2020, and then a press release, to withdraw the exemption of wearing face mask for respiratory patients.5 Therefore, many asthma, COPD and other respiratory patients did not protect themselves and, if infected, might be contagious to others.6,7 The objective of this report was to cross-sectionally assess the use of face masks in adult smokers according to self-reported diseases using data from the EUREST-PLUS Spain Survey.The EUREST-PLUS Spain Survey, part of the International Tobacco Control Policy Evaluation (ITC) Project, surveyed a national representative sample of adult smokers in Spain in 2021, as a follow up survey that started in 2016.8,9 This longitudinal study started in 2016 (wave 1), with follow-up surveys in 2018 (wave 2) and 2021 (wave 3).In this last wave, we recontacted all respondents in wave 2 and recruited new smokers to replace those lost due to attrition, using the same sampling frame as the one used in the previous surveys, and the final sample included 1006 respondents.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".