The Effect of Perceived Threats and Response Efficacy on Adaptation to Smog: An Instrumental Variables Design
Bibliographic record
Abstract
Threats and response efficacyperceptions are core conceptsof the protection motivationtheory, and recent years have witnessed a considerable growth of research on the effect of thesefactors on adaptation to air pollution. However, few studies use appropriate designs to deal with endogeneity issues, a situation that raises serious questions on the validity of their findings. To overcome this problem, this study uses the instrumental variables method to test the effect of perceived threats and response efficacy on adaptation to smog episodes. The results of this study show that the conjunction of a moderate to high perception of threats with a high perception of response efficacy is positively associated with the adoption of the recommended behavior. The increase of perceived threats does not seem to have an effect on the behavior of individuals with low response efficacy perception. Moreover, change in perceived response efficacy does not lead to any change in the behavior of individuals with low threat perceptions. Concerning policy implications, this study suggests that smog warnings and health communication campaigns could be more effective if they provide accurate information simultaneously on air pollution level, its adverse effects, and advice on how to mitigate these effects.
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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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".