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Medical genetics and patient use of the Internet

2001· article· en· W1572165486 on OpenAlexaff
Susan Christian, SA Kieffer, N. J. LEONARD

Bibliographic record

VenueClinical Genetics · 2001
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of Alberta HospitalAlberta Hospital Edmonton
Fundersnot available
KeywordsThe InternetMedical geneticsReferralMedicineMedical informationFamily medicineInternet privacyPsychologyGeneticsWorld Wide WebComputer scienceBiology

Abstract

fetched live from OpenAlex

Clinical experience suggests that the Internet is increasingly becoming a resource for patients seen in medical genetics. A prospective analysis was performed exploring patient use of the Internet prior to attending a medical genetics appointment. We administered 200 questionnaires assessing: 1) the frequency of patient use of the Internet for genetic information, 2) factors associated with Internet use, 3) patient assessment of the value of the information, and 4) patient views of the responsibility of medical genetics professionals to be familiar with Internet information. Results show that 77% (153/200) of patients have access to the Internet of which 29% (44/153) report searching the Internet for genetic information. A correlation was found between patient use of the Internet and reason for referral (p<0.001), presence of a specific diagnosis (p<0.001), and frequency of Internet use (p<0.05). Overall, 80% (33/41) of patients found Internet information useful. Seventy-four percent (115/155) believed that medical genetics professionals have a responsibility to review relevant Internet sites for accuracy and 80% (123/153) felt that professionals should provide their patients with appropriate and useful Internet sites. These results suggest that the role of medical genetics professionals is changing as a result of the development of the Internet.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.223
GPT teacher head0.535
Teacher spread0.312 · 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 designObservational
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

Citations22
Published2001
Admission routes1
Has abstractyes

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