The Moderating Effect of COVID-19 Risk Perception on the Relationship Between Empathy and COVID-19 Volunteer Behavior: A Cross-Sectional Study in Jiangsu, China
Bibliographic record
Abstract
The health system has encountered great challenges since the COVID-19 outbreak, volunteers are urgently needed in every situation during this crisis. The current study aimed to explore the relationship between empathy and COVID-19 volunteer behavior, along with the moderating role of COVID-19 risk perception in the above relationship. The cross-sectional survey was conducted online using Wenjuanxing from February 12th to March 16th, 2021, in Jiangsu, China. A total of 1,486 participants completed the Toronto Empathy COVID-19 volunteer behavior and COVID-19 risk perception questionnaires. The SPSS PROCESS macro was yielded to examine the moderating effect. Simple slopes analysis was conducted to detect the associations between empathy and COVID-19 volunteer behavior at three levels of the COVID-19 risk perception. The Johnson-Neyman (J-N) technique was used to calculate where the moderating effect is significance. Results showed that empathy was positively related with COVID-19 volunteer behavior (β= 0.080, p < 0.001). COVID-19 risk perception played a moderation effect on association between empathy and COVID-19 volunteer behavior (β = −0.005, p < 0.001), the greater the levels of COVID-19 risk perception, the weaker the associations between empathy and COVID-19 volunteer behavior. The J-N test showed the association between empathy and COVID-19 volunteer behavior was no longer significant when values of COVID-19 risk perception was >10.71. Current findings could enlighten researchers and policy makers, that fostering volunteerism among public during crisis situation through arousing more empathy and reducing unnecessary risk perception of the public.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".