Data and alternative models describing the associations among non-infection pandemic stress, event-related rumination, depression, and anxiety
Bibliographic record
Abstract
Here we present cross-sectional data collected from 1507 participants through the Qualtrics online survey platform. Participants were recruited from Reddit, Facebook, and the Queen's University undergraduate participant pool, and were instructed to complete a pandemic stress survey, the Beck Depression Inventory-II (BDI-II) [1], the Beck Anxiety Inventory (BAI) [2], a modified version of Event-Related Rumination Inventory (ERRI) [3], and a demographics questionnaire. For the 1069 participants who were not exposed to COVID-19 infection, we calculated the sum of each scale/subscale and performed a multiple mediation analysis using MPlus. The results indicated that three models (one primary model and two alternative models) had comparable statistical power to explain the variance as we tested different configurations of predictor, mediator, and outcome variables. Given the cross-sectional nature of the present study, we could not conclude which model was most valid. Therefore, we share our original data and tested models here for others to use. They are useful for researchers who wish to replicate our results, conduct new analyses with these data, or design future studies.
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 | 0.064 | 0.130 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".