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Record W7165546511 · doi:10.2196/88632

Impact of Repeated Exposure to Polarized Health-Related News on Explicit and Implicit Attitudes Toward Dietary Supplements: Online Experimental Study (Preprint)

2025· article· en· W7165546511 on OpenAlexvenueno aff
Eugen‐Călin Secară, Nicolae-Adrian Opre

Bibliographic record

VenueJMIR Infodemiology · 2025
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionAffect (linguistics)Action (physics)Set (abstract data type)

Abstract

fetched live from OpenAlex

Background: Repetition is a central feature of digital news consumption, where engagement-driven algorithms often expose users to similar health-related content. Prior research suggests that repeated exposure can influence perceived truth and evaluation, but most studies use brief or decontextualized stimuli and have rarely distinguished between explicit and implicit attitudes. Little is known about how repeated exposure to full-length, polarized health news shapes explicit and implicit attitudes, particularly toward familiar products such as dietary supplements. Objective: This study aims to examine whether 2 weeks of exposure to positively, negatively, or mixed-valence health news articles would alter explicit and implicit attitudes toward dietary supplements, and whether engagement mediated these effects or initial attitudes moderated them. Methods: In a preregistered 4 (group: PRO, CON, MIX, and control) × 3 (time: T0, T1, and T2) mixed experimental study, 228 participants (174 women, PRO: n=68, CON: n=51, MIX: n=52, control: n=57; mean age 2.81, SD 4.88 years) were randomly assigned to receive one full-length article per day for 2 weeks. Articles presented positive (PRO), negative (CON), mixed (MIX), or neutral space-related information (control). Explicit attitudes toward dietary supplements (perceived efficiency, harmfulness, and willingness to recommend) were assessed with visual analog scales, and implicit attitudes with an Implicit Association Test at baseline, 1 week, and 2 weeks. Mixed analysis of covariances (ANCOVAs) controlled for self-reported exposure to health-related news. Mediation, moderation, and moderated mediation analyses examined whether time spent reading and baseline attitudes influenced change. Results: The change in implicit attitudes was not significant (F6, 428=1.90; P=.08). In contrast, explicit attitudes showed a significant group × time interaction (F5.04, 428=14.34; P<.001). Explicit attitudes became more favorable in the PRO group (MΔ=14.86, SE=5.93, d=0.30; P=.02) and less in the CON (MΔ=-53.42, SE=7.35, d=1.02; P<.001) and MIX groups (MΔ=-24.56, SE=6.77, d=0.50; P=.001). At T2, the CON group reported lower explicit attitudes than the control group (MΔ=-32.84, SE=1.75, d=0.32; P=.02), and the PRO group scored higher than the CON and MIX groups (MΔ=52.08, d=0.52; P<.001 and MΔ=27.07, d=0.29; P=.03). Exploratory item analyses showed large effects of negative exposure on perceived harmfulness and recommendability. No mediation by reading time and no moderation by baseline attitudes were supported. Conclusions: Repeated exposure to polarized health-related news shifted explicit but not implicit attitudes toward dietary supplements. Negative content exerted the strongest influence. Engagement and prior attitudes did not meaningfully shape these outcomes. These findings suggest that even brief, routine exposure to polarized health information may accumulate into explicit evaluative change, underscoring the importance of balanced digital news environments.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.098
GPT teacher head0.501
Teacher spread0.403 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations0
Published2025
Admission routes1
Has abstractno

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