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Record W2346590829 · doi:10.5430/jha.v5n4p55

Chronic disease and use of online health information and online health services

2016· article· en· W2346590829 on OpenAlexvenueno aff
Rita Mano

Bibliographic record

VenueJournal of Hospital Administration · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsOddsLogistic regressionMedicineOrdered logitHealth careHealth informationLogitEmpowermentHealth servicesSample (material)Chronic diseaseDiseaseOdds ratioFamily medicineEnvironmental healthPathologyComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

This study examines the factors associated with computer use for the self-management of health among individuals diagnosed with chronic diseases (CD) in Israel. We distinguish between: (1) access to online health information, and (2) use of online health services (OHS). A geographic representative sample comprising 2008 individuals was contacted. 1406 individuals (67.6%) reported using the computer for health concerns. Four conditions – heart, cancer, diabetes and hypertension – were identified (N = 225). Using a series of logit regression models it is shown that CD increases access to online health information (OHI) but its effect of use of OHS is specific to: (1) type of CD, i.e., heart condition, and (2) type of provided service, i.e., medical updates. These results indicate that while computer use increases the odds for higher empowerment this may not necessarily lead to higher use of OHS provided by the healthcare provider among individuals diagnosed with CD decreasing the likelihood for better self-management. Implications for health policy are discussed.

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)0.000
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 score0.343
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.005
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 head0.031
GPT teacher head0.413
Teacher spread0.383 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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