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Record W1680777985 · doi:10.2196/resprot.4010

The Prevalence of Online Health Information Seeking Among Patients in Scotland: A Cross-Sectional Exploratory Study

2015· article· en· W1680777985 on OpenAlexvenueno aff
Julia Moreland, Tara French, Grant Cumming

Bibliographic record

VenueJMIR Research Protocols · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsInformation seekingThe InternetExploratory researchMedicineFamily medicinePopulationHealth informationInformation seeking behaviorHealth careHealth Information National Trends SurveyPsychologyEnvironmental healthWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Online health information seeking is an activity that needs to be explored in Scotland. While there are a growing number of studies that adopt a qualitative approach to this issue and attempt to understand the behaviors associated with online health information seeking, previous studies focusing on quantifying the prevalence and pattern of online health seeking in the United Kingdom have been based on Internet users in general. OBJECTIVE: This exploratory study sought to describe the prevalence of online health information seeking in a rural area of Scotland based on primary data from a patient population. METHODS: A survey design was employed utilizing self-completed questionnaires, based on the Pew Internet and American Life Project; questionnaires were distributed among adult patients in 10 primary care centers in a rural community in Scotland. RESULTS: A convenience sample of 571 (0.10% of the total population in Grampian, N=581,198) patients completed the questionnaire. A total of 68.4% (379/554) of patients had previously used the Internet to acquire health information. A total of 25.4% (136/536) of patients consulted the Internet for health information regarding their current appointment on the day surveyed; 34.6% (47/136) of these patients were influenced to attend their appointment as a result of that online health information. A total of 43.2% (207/479) of patients stated the health information helped improve their health and 67.1% (290/432) indicated that they had learned something new. A total of 34.0% (146/430) of patients talked to a health professional about the information they had found and 90.0% (376/418) reported that the information was useful. In total, 70.4% (145/206) of patients were concerned about obtaining health information online from reliable sources. A total of 67.1% (139/207) of patients were concerned that a health site may sell their personal information, yet only 6.7% (36/535) checked the privacy policy of the site visited. However, 27.9% (55/197) of patients were not concerned about their employer finding out what health sites they visited, whereas 37.5% (78/208) were concerned that others would find out. CONCLUSIONS: The results suggest that online health information-seeking behavior influences offline health-related behavior among the population surveyed. Patient attitudes to online health information seeking were focused on issues relating to trust, reliability, privacy, and confidentiality. This study provides support for the growing phenomenon of an empowered, computer-literate, health information consumer, and the impact of this phenomenon must be considered in the context of the patient-health professional dynamic. The unpredictable nature of human thought and action in relation to this field of study requires an ongoing program of ethnographic research, both physical and virtual, within a Health Web Science framework. This study has provided a baseline of the prevalence of online health information seeking in the Grampian region of Scotland.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.399
GPT teacher head0.648
Teacher spread0.248 · 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 designObservational
Domainnot available
GenreProtocol

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".

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Citations75
Published2015
Admission routes1
Has abstractyes

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