Conceptual replication and extension of health behavior theories' predictions in the context of COVID‐19: Evidence across countries and over time
Bibliographic record
Abstract
Abstract Virus mitigation behavior has been and still is a powerful means to fight the COVID‐19 pandemic irrespective of the availability of pharmaceutical means (e.g., vaccines). We drew on health behavior theories to predict health‐protective (coping‐specific) responses and hope (coping non‐specific response) from health‐related cognitions (vulnerability, severity, self‐assessed knowledge, efficacy). In an extension of this model, we proposed orientation to internal (problem‐focused coping) and external (country capability) coping resources as antecedents of health protection and hope; health‐related cognitions were assumed as mediators of this link. We tested these predictions in a large multi‐national multi‐wave study with a cross‐sectional panel at T1 (Baseline, March‐April 2020; N = 57,631 in 113 countries) and a panel subsample at two later time points, T2 (November 2020; N = 3097) and T3 (April 2021; N = 2628). Multilevel models showed that health‐related cognitions predicted health‐protective responses and hope. Problem‐focused coping was mainly linked to health‐protective behaviors (T1‐T3), whereas country capability was mainly linked to hope (T1‐T3). These relationships were partially mediated by health‐related cognitions. We conceptually replicated predictions of health behavior theories within a real health threat, further suggesting how different coping resources are associated with qualitatively distinct outcomes. Both patterns were consistent across countries and time.
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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 | 0.038 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".