The trajectory of COVID-19 pandemic and handwashing adherence: findings from 14 countries
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has affected people's engagement in health behaviors, especially those that protect individuals from SARS-CoV-2 transmission, such as handwashing/sanitizing. This study investigated whether adherence to the World Health Organization's (WHO) handwashing guidelines (the outcome variable) was associated with the trajectory of the COVID-19 pandemic, as measured by the following 6 indicators: (i) the number of new cases of COVID-19 morbidity/mortality (a country-level mean calculated for the 14 days prior to data collection), (ii) total cases of COVID-19 morbidity/mortality accumulated since the onset of the pandemic, and (iii) changes in recent cases of COVID-19 morbidity/mortality (a difference between country-level COVID-19 morbidity/mortality in the previous 14 days compared to cases recorded 14-28 days earlier). METHODS: The observational study (#NCT04367337) enrolled 6064 adults residing in Australia, Canada, China, France, Gambia, Germany, Israel, Italy, Malaysia, Poland, Portugal, Romania, Singapore, and Switzerland. Data on handwashing adherence across 8 situations (indicated in the WHO guidelines) were collected via an online survey (March-July 2020). Individual-level handwashing data were matched with the date- and country-specific values of the 6 indices of the trajectory of COVID-19 pandemic, obtained from the WHO daily reports. RESULTS: Multilevel regression models indicated a negative association between both accumulation of the total cases of COVID-19 morbidity (B = -.041, SE = .013, p = .013) and mortality (B = -.036, SE = .014 p = .002) and handwashing. Higher levels of total COVID-related morbidity and mortality were related to lower handwashing adherence. However, increases in recent cases of COVID-19 morbidity (B = .014, SE = .007, p = .035) and mortality (B = .022, SE = .009, p = .015) were associated with higher levels of handwashing adherence. Analyses controlled for participants' COVID-19-related situation (their exposure to information about handwashing, being a healthcare professional), sociodemographic characteristics (gender, age, marital status), and country-level variables (strictness of containment and health policies, human development index). The models explained 14-20% of the variance in handwashing adherence. CONCLUSIONS: To better explain levels of protective behaviors such as handwashing, future research should account for indicators of the trajectory of the COVID-19 pandemic. TRIAL REGISTRATION: Clinical Trials.Gov, # NCT04367337.
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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 | 0.001 | 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".