COVID-19 behavior determinants dataset
Bibliographic record
Abstract
The COVID-19 Behavior Determinants Database (http://covid19-database.com) is a research project that examined the sociodemographic and psychological determinants of COVID-19 related attitudes and behaviors. It is a comprehensive web-based survey that was administered to adults ages 18 or older (total n=8070) from the United States of America (n = 5326), including the four most populous states, specifically New York, California, Florida, and Texas, and Canada (n = 2744), including all provinces, except Quebec. The survey was collected at three timepoints, May 2020 (n=1019), July 2020 (n=4027), and March 2021 (n=3024). Participants provided detailed sociodemographic information and completed a battery of psychological assessments. Participants also provided information about prior testing for COVID-19 and perceived seriousness of COVID-19 and the need for current physical (social) distancing restrictions. The database is helpful to researchers and public health policy decision-makers who are interested in investigating and identifying the determinants of COVID-19 related attitudes and behaviors in North America.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.023 |
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".