‘We are all in the same boat’: How societal discontent affects intention to help during the <scp>COVID</scp>‐19 pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The coronavirus disease 2019 (COVID‐19) pandemic has caused a global health crisis. Consequently, many countries have adopted restrictive measures that caused a substantial change in society. Within this framework, it is reasonable to suppose that a sentiment of societal discontent, defined as generalized concern about the precarious state of society, has arisen. Literature shows that collectively experienced situations can motivate people to help each other. Since societal discontent is conceptualized as a collective phenomenon, we argue that it could influence intention to help others, particularly those who suffer from coronavirus. Thus, in the present study, we aimed (a) to explore the relationship between societal discontent and intention to help at the individual level and (b) to investigate a possible moderating effect of societal discontent at the country level on this relationship. To fulfil our purposes, we used data collected in 42 countries ( N = 61,734) from the PsyCorona Survey, a cross‐national longitudinal study. Results of multilevel analysis showed that, when societal discontent is experienced by the entire community, individuals dissatisfied with society are more prone to help others. Testing the model with longitudinal data ( N = 3,817) confirmed our results. Implications for those findings are discussed in relation to crisis management. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement .
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it