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Record W3210177747 · doi:10.1177/00222437211060854

Tainted by Stigma: The Interplay of Stigma and Moral Identity in Health Persuasion

2021· article· en· W3210177747 on OpenAlexaff
Chethana Achar, Lea Dunn, Nidhi Agrawal

Bibliographic record

VenueJournal of Marketing Research · 2021
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsPersuasionStigma (botany)Social psychologyPsychologyMoralityIdentity (music)Health communicationPsychiatryPolitical scienceLaw

Abstract

fetched live from OpenAlex

The current research examines the interactive effect of consumers’ moral identity and risk factor stigma on health message effectiveness. The authors theorize that engaging in advocated health behaviors has moral associations; however, a stigmatized risk factor in a message “taints” the morality of the advocated health behavior. Thus, consumers with high (vs. low) moral identity are more likely to comply with health messages when risk factor stigma is low, and this positive moral identity effect is undermined when risk factor stigma is high. The authors test stigma's threat to moral identity by measuring defensive processing (Studies 1 and 2) and the attenuating effect of self-affirmation on the negative effect of stigma (Studies 3 and 4). They apply the stigma-by-association principle to develop and test a messaging intervention (Study 5). The studies suggest that, depending on whether a health message contains stigmatized risk factors, marketers could employ a combination of tactics such as activating moral identity, offering self-affirming message frames, and/or highlighting low-stigma risk factors to bolster message effectiveness.

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)0.025
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.003
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.123
GPT teacher head0.530
Teacher spread0.407 · 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.

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

Citations19
Published2021
Admission routes1
Has abstractyes

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