MétaCan
Menu
Back to cohort
Record W36126486

Websites most frequently used by physician for gathering medical information.

2006· article· en· W36126486 on OpenAlexaboutno aff
Gianluca De Leo, Cynthia LeRouge, Claudia Ceriani, Fred Niederman

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsThe InternetMedical informationQuarter (Canadian coin)Internet privacyWeb siteMedical recordPatient careWorld Wide WebOrder (exchange)MedicineFamily medicineMedical educationComputer scienceBusinessNursingGeography
DOInot available

Abstract

fetched live from OpenAlex

Physicians' use of the Internet to gather medical information has increased in recent years. Several studies have been conducted to explore the implications of this use on patient education, the physician-patient relationship, and diagnosis/decision making. In order to better understand the current and future implications of Internet use on patient care activities, it is important to know the Internet sources physicians prefer to consult. The objective of this study was to determine the Internet sources of information physicians most often use to gather medical information. This study demonstrated that the vast majority of physicians indicate they access a targeted site rather than utilize a search engine (such as Google) to gather medical information. Of the targeted site types, most physicians indicate they use 1) edited/secondary data sources as their primary medical information data retrieving, 2) about one quarter of the physicians surveyed indicated research databases which provide access to medical journal publications 3) a minority of physicians use sites dedicated to their specialized area and 4) a small percentage use medical web site portals.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
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 head0.029
GPT teacher head0.354
Teacher spread0.324 · 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 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

Citations54
Published2006
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicHealth Literacy and Information AccessibilityFrench-language works237,207