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Record W3020599450 · doi:10.1177/0022243720912443

Fear of Detection and Efficacy of Prevention: Using Construal Level to Encourage Health Behaviors

2020· article· en· W3020599450 on OpenAlexaff
Chethana Achar, Nidhi Agrawal, Meng‐Hua Hsieh

Bibliographic record

VenueJournal of Marketing Research · 2020
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsPersuasionPsychologyConstrual level theoryPerceptionHealth belief modelSocial psychologyTraitMental illnessClinical psychologyHealth promotionMental healthPsychiatryPublic healthMedicine

Abstract

fetched live from OpenAlex

This research examines the psychological processes and factors that shape illness-detection versus illness-prevention health actions. Four experiments using contexts of mental health, skin cancer, and breast cancer show that illness detection evokes fear, which undermines engagement in detection behaviors. Considering detection at low (vs. high) levels of thought reduced fear and increased health persuasion. Illness prevention is driven by self-efficacy perceptions and considering prevention at high (vs. low) levels of thought increases persuasion. In further evidence of process, trait fear moderated the detection effects, and dispositional self-efficacy moderated the prevention effects. As an intervention, framing a detection action as serving illness-prevention goals increased people’s likelihood of engaging with an online breast cancer detection tool. These findings illuminate the psychology of detection as being distinct from the psychology of prevention, identify the role of fear in the consideration of health behaviors, and show contexts in which construal levels have divergent effects on health persuasion.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.916
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.368
GPT teacher head0.546
Teacher spread0.178 · 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

Citations34
Published2020
Admission routes1
Has abstractyes

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