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Using Eye Tracking Technology to Determine the Most Effective Viewing Format and Content for Osteoporosis Prevention Print Advertisements

2011· article· en· W1506117099 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Applied Biobehavioral Research · 2011
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsQueen's University
FundersFaculty of Arts and Sciences
KeywordsOsteoporosisEye trackingClothingPsychologyMedicineMultimediaAdvertisingComputer scienceInternal medicineArtificial intelligence

Abstract

fetched live from OpenAlex

In series of three experiments young women's attention patterns were analyzed using eye tracking to deteremine the most effective viewing format and message content for presenting osteoporosis prevention ads. In Experiment 1A ads including both images and text attracted more attention than the image‐only or text‐only formats, p < .01. Experiment 1B revealed that osteoporosis ads attract significantly less attention than fashion and beauty ads ( p < .01) and exercise apparel ads ( p < .01). The last experiment revealed that osteoporosis ads featuring exercise messages were more effective at capturing attention than those promoting increased calcium and vitamin D consumption ( p < .01). These findings may inform the way in which future osteoporosis prevention ads are created.

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.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.918
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.439
GPT teacher head0.536
Teacher spread0.097 · 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