Social support, perceived risk and the likelihood of COVID-19 testing and vaccination: cross-sectional data from the United Kingdom
Bibliographic record
Abstract
Two samples of 227 and 214 adults completed surveys of social support, perceived risk of COVID-19 and COVID-19 preventive activity - in Study 1 likelihood of testing was examined and in Study 2 likelihood of both testing and vaccination were examined during the COVID-19 pandemic in the United Kingdom. Path analysis showed, in Study 1, that access to help (as an indicator of social support) had a direct effect on likelihood of testing and indirect effects through self-efficacy, perceived risk and preventive behavior; and, in Study 2, that neighborhood identification (as an indicator of social support) had a direct effect on likelihood of testing and indirect effects on likelihood of both testing and vaccination through the mediators of strength of social network, loneliness, perceived risk of COVID-19, and preventive activity. Both studies suggest that level of social support (conceptualized in different ways) is an important determinant of COVID-19 testing and Study 2 shows it is also a determinant of likelihood of vaccination. As resurgences of COVID-19 occur, it will be necessary to monitor the likelihood of COVID-19 testing and vaccination behaviors and, especially, to promote confidence in the latter in individuals with decreased access to social support.
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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".