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Record W2161788636 · doi:10.1177/1049732307305199

Health Information—Seeking Behavior

2007· article· en· W2161788636 on OpenAlexaff
Sylvie Lambert, Carmen G. Loiselle

Bibliographic record

VenueQualitative Health Research · 2007
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychosocialPsychological interventionPsychologyInformation seeking behaviorCoping (psychology)Applied psychologyMEDLINEInformation seekingSocial psychologyComputer scienceClinical psychologyPsychotherapistInformation retrieval

Abstract

fetched live from OpenAlex

Seeking information about one's health is increasingly documented as a key coping strategy in health-promotive activities and psychosocial adjustment to illness. In this article, the authors critically examine the scientific literature from 1982 to 2006 on the concept of health information-seeking behavior (HISB) to determine its level of maturity and clarify the concept's essential characteristics. A principle-based method of concept analysis provides the framework for exploring the nature of HISB. The authors reviewed approximately 100 published articles and five books reporting on HISB. Although HISB is a popular concept used in various contexts, most HISB definitions provide little insight into the concept's specific meanings. The authors describe the concept's characteristics, contributing to a clearer understanding of HISB, and discuss operationalizations, antecedents, and outcomes of HISB. Such an analysis of HISB might guide further theorizing on this highly relevant concept and assist health care providers in designing optimal informational interventions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.545
GPT teacher head0.721
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), 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

Citations916
Published2007
Admission routes1
Has abstractyes

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