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Record W2228363185 · doi:10.1093/pubmed/fdv177

Women's perceptions of heart disease and breast cancer and the association with media representations of the diseases

2015· article· en· W2228363185 on OpenAlexafffund
Tanya R. Berry, Jodie A. Stearns, Kerry S. Courneya, Kerry R. McGannon, Colleen M. Norris, Wendy M. Rodgers, John C. Spence

Bibliographic record

VenueJournal of Public Health · 2015
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsLaurentian UniversityUniversity of Alberta
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsBreast cancerHeart diseaseDiseaseMedicineSeriousnessPerceptionCancerInternal medicinePsychology

Abstract

fetched live from OpenAlex

This research examined differences in perceptions of heart disease compared with breast cancer and if the differences are reflected in media presentations of the diseases. Relationships of differences in perceptions to demographic groups, heart disease risk factors and health behaviors were examined. Study 1 was a quantitative content analysis of articles and advertisements related to heart disease or breast cancer. There were greater perceptions of susceptibility, preventability and controllability of heart disease and lower perceptions regarding seriousness, fearfulness and extent to which family history determines disease development of heart disease compared with breast cancer. Five times more pieces related to breast cancer were found compared with heart disease. Study 2 was a survey of 1524 women. More articles and advertisements about breast cancer than heart disease were found, and survey participants reported seeing significantly more breast cancer than heart disease media. Younger women had greater perceived susceptibility of breast cancer relative to heart disease while the content analysis revealed that the heart disease pieces were more likely to feature women older than 40 years of age. This research is an important step in the development of theories regarding causal effects of media on health perceptions and behaviors.

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 score0.111
Threshold uncertainty score0.164

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.080
GPT teacher head0.420
Teacher spread0.340 · 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

Citations17
Published2015
Admission routes2
Has abstractyes

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