Motivations for the Practice of Self-Care Measures Applicable to Mitigate COVID-19 Pandemic
Bibliographic record
Abstract
Objective: To identify relevant factors influencing the practice of self-care measures for prevention of the ongoing COVID-19 preventions based on prior evidence-based experiences. Method: We conducted a literature review of empirical studies conducted between the years 2000 and 2020 focusing on self-care measures in a pandemic situation. Result: Of the 250 studies identified, 19 studies met inclusion criteria. Sixty-three percent of the eligible studies reported handwashing, 21% reported social distancing, facemask wearing, 11% reported social avoidance and information-seeking behaviour. The identified factors motivating these practices include risk perception, health education and social trust. Conclusion: We found that public health agencies commonly recommend self-care measures during pandemics. The adherence to them depends on individuals' perception of risk, knowledge about the situation, trust in the government agencies providing the recommendations and empathy that can motivate adherence. Practice Implication: The public, researchers, and policymakers could learn from the past and present situation to understand what measures are proven relevant and what factors could motivate adherence. More emphasis could be placed on the role of individuals in health promotion and disease prevention as they have been proven to be helpful.
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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 | 0.024 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".