MétaCan
Menu
Back to cohort
Record W3194518358 · doi:10.1111/risa.13814

The Effect of Perceived Threats and Response Efficacy on Adaptation to Smog: An Instrumental Variables Design

2021· article· en· W3194518358 on OpenAlexafffund
Kaddour Mehiriz, Pierre Gosselin

Bibliographic record

VenueRisk Analysis · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversité LavalInstitut National de Santé Publique du Québec
FundersHealth CanadaInstitute for Health Metrics and Evaluation
KeywordsEndogeneityPerceptionInstrumental variableAdaptation (eye)PsychologyRisk perceptionEnvironmental healthSelf-efficacyClimate changeSocial psychologyMedicineEconometricsEconomics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.269
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueRisk AnalysisSame topicEnvironmental Education and SustainabilityFrench-language works237,207