Do Empathy, Prosociality, and Cultural Orientation Among Age and Gender Cohorts Predict Covid-19 Vaccine Status?
Bibliographic record
Abstract
Past research has found that psychological factors and dimensions of cultural orientation can be salient predictors of general as well as Covid-19-specific vaccine hesitancy beliefs and attitudes. Gender and age have also been shown to influence these relationships, especially during the Covid-19 pandemic. The current study aimed to extend on past literature to explore psychological factors like empathy and prosociality, and dimensions of individualism and collectivism orientations, to examine whether they predicted vaccine status in different age and gender cohorts. Empathy was measured using the Toronto Empathy Questionnaire developed by Spreng et al. (2009); prosociality was measured using the Prosociality Scale for adults developed by Caprara et al. (2005); and individualism and collectivism orientations were assessed using Triandis & Gelfland’s (1998) Individualism and Collectivism Scale. Results indicated that empathetic women and individuals with vertical collectivism, horizontal individualism, and horizontal collectivism orientations were associated with significantly higher odds of being voluntarily vaccinated. Future research might expand on the literature concerning predictors of Covid-19 vaccination by specifically looking at motives of voluntary vaccination since it is a largely understudied area. Researchers might also investigate the associations between empathy and prosociality in voluntary Covid-19 vaccination more deeply because past literature has indicated that they are significant predictors of vaccine acceptance.\nKeywords: Empathy, Prosociality, Horizontal and Vertical Individualism and Collectivism, Age, Gender, Covid-19 Vaccine Status, Voluntary Vaccination
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".