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Record W2163068962 · doi:10.1093/heapro/dag409

Shortcomings of health information on the Internet

2003· article· en· W2163068962 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

VenueHealth Promotion International · 2003
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsThe InternetDisseminationHarmInternet privacyPopulationHealth informationInformation DisseminationHealth carePublic healthHRHISMedicineHealth educationPublic relationsEnvironmental healthBusinessNursingWorld Wide WebPsychologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Disseminating health and medical information on the Internet can improve knowledge transfer from health professionals to the population, and help individuals to maintain and improve their health. There are currently several medical information websites that directly target the general population with the aim of providing information about health problems, self-care and prevention. However, this new technology also hides several shortcomings, such as: (i) uneven quality of medical information available on the Internet; (ii) difficulties in finding, understanding and using this information; (iii) lack of access for the unconnected population; and (iv) the potential for harm and risks of over-consumption. To be able to overcome these dangers, it is important that public health practitioners and health professionals be involved in the design, dissemination and evaluation of Web-based health and medical information.

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)0.009
metaresearch head score (Gemma)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.788
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.106
GPT teacher head0.467
Teacher spread0.362 · 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