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Web Site Development: Applying Aesthetics to Promote Breast Health Education and Awareness

2002· article· en· W1970830497 on OpenAlexaff
Barbara Thomas, SUSAN B. GOLD SMITH, Anne Forrest, Renee L. Marshall

Bibliographic record

VenueCIN Computers Informatics Nursing · 2002
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsWeb siteProcess (computing)Web applicationWorld Wide WebWeb standardsWeb Accessibility InitiativeComputer scienceVariety (cybernetics)Web designWeb developmentMultimediaWeb intelligenceThe Internet

Abstract

fetched live from OpenAlex

This article describes the process of establishing a Web site as part of a collaborative project using visual art to promote breast health education. The need for a more "user-friendly" comprehensive breast health Web site that is aesthetically rewarding was identified after an analysis of current Web sites available through the World Wide Web. Two predetermined sets of criteria, accountability and aesthetics, were used to analyze these sites and to generate ideas for creating a breast health education Web site using visual art. Results of the analyses conducted are included as well as the factors to consider for incorporating into a Web site. The process specified is thorough and can be applied to establish a Web site that is aesthetically rewarding and informative for a variety of educational purposes.

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)0.011
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.003
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.022
GPT teacher head0.303
Teacher spread0.281 · 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 designNot applicable
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

Citations5
Published2002
Admission routes1
Has abstractyes

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