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Record W2030173474 · doi:10.4018/jhisi.2009071001

Internet as a Source of Health Information and its Perceived Influence on Personal Empowerment

2009· article· en· W2030173474 on OpenAlexaffabout
Guy Paré, Jean-Nicolas Malek, Claude Sicotte, Marc Lemire

Bibliographic record

VenueInternational Journal of Healthcare Information Systems and Informatics · 2009
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversité de MontréalHEC Montréal
Fundersnot available
KeywordsThe InternetEmpowermentPersonally identifiable informationInternet privacyPsychologyHealth careInformation source (mathematics)PopulationSample (material)Health informationPublic healthPublic relationsMedicineNursingEnvironmental healthWorld Wide WebPolitical scienceComputer science

Abstract

fetched live from OpenAlex

The primary aim of this study is twofold. First, the authors seek to identify the factors that influence members of the general public to conduct Internet searches for health information. Their second intent is to explore the influence such Internet use has on three types of personal empowerment. In the summer of 2007 the authors conducted a household sample survey of a population of Canadian adults. A total of 261 self-administered questionnaires were returned to the researchers. Our findings indicate that use of the Internet as a source of health information is directly related to three main factors: sex, age and the individual’s perceived ability to understand, interpret and use the medical information available online. Further, their results lend support to the notion that using the Internet to search for information about health issues represents a more consumer based and participative approach to health care. This study is one of the first to relate Internet use to various forms of personal empowerment. This area appears to have great potential as a means by which consumers can become more empowered in managing personal health issues.

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)0.004
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.790

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.011
Open science0.0000.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.407
Teacher spread0.370 · 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 designQualitative
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

Citations13
Published2009
Admission routes2
Has abstractyes

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