Adherence to the Recommendations from the Portuguese General Directorate of Health (GDH) during the COVID-19 pandemic: fear or prosocial behaviour?
Bibliographic record
Abstract
Introduction During a public health crisis, preventive measures are essential. However, to make them effective, all citizens must be engaged. Objectives To analyse the differential role of individual and contextual variables in the adherence to public health recommendations. Methods 1376 adults (70.5% female; mean age=35.55±14.27) completed a survey between September/2020 and May/2021 with: Adherence Scale to the Recommendations during COVID-19 (ASR-COVID19; evaluates three dimensions of adherence), Fear of Covid-19 Scale (FC19S) and Toronto and Coimbra Prosocial Behaviour Questionnaire (ProBeQ; assesses empathy and altruism). Results Adherence did not differ between individuals with or without personal or family history of COVID-19 infection. ASR-COVID19 and all dimensions were positively correlated to ProBeQ’s altruism and empathy (from r=.32 to r=.54); FCV19S correlated positively to total adherence score and house sanitation (from r=.18 to r=.26; all p<.01). Linear regressions revealed that altruism and empathy (first model), as well as fear of Covid-19 (second model), were significant predictors of adherence; however, while the first model explained ≅28% of its variance, the second (FCV19S as independent variable) only explained ≅3%. Regression models performed in a subsample of participants with personal or family history of COVID-19 revealed that only empathy, but not altruism, was a significant predictor of adherence; in this subsample, fear was no longer a significant predictor of adherence, except for lockdown and use of teleservices. Conclusions Based on our results, we suggest health care providers and public health campaigns should take into consideration social solidarity and altruism, as well as previous experiences, when appealing to public’s engagement in health behaviour. Disclosure No significant relationships.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".