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Record W1976780849 · doi:10.1177/1460458207086331

Use of health-related information from the Internet by English-speaking patients

2008· article· en· W1976780849 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHealth Informatics Journal · 2008
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsThe InternetIdentification (biology)Health informationMedical informationHealth carePsychologyScientific literatureMedical educationMedicineInternet privacyFamily medicineComputer scienceWorld Wide WebPolitical science

Abstract

fetched live from OpenAlex

The aim of this research is to determine the kinds of health-related information that patients seek more often from websites written in English, and at which stages of the healthcare decisional process they use this information more intensively. A quantitative study was performed. Canadian English-speaking patients who have long-term diseases and who use the Internet completed an 18-item questionnaire online. Respondents were questioned about the categories of health-related websites they visit the most (scientific, general, commercial websites, or discussion groups) and the stages of the medical decisional process during which they use the information obtained (identification of possible treatments, treatment choice, and treatment application or follow-up). Results show that respondents use Internet information displayed in English mostly at the stages of identification of possible treatments (94.2%) and treatment application or follow-up (86%). At these two stages, patients look more often for information from scientific websites.

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.009
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.092
GPT teacher head0.385
Teacher spread0.293 · 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