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Record W2810184620 · doi:10.2196/ijmr.9803

Characterizing Websites That Provide Information About Complementary and Integrative Health: Systematic Search and Evaluation of Five Domains

2018· article· en· W2810184620 on OpenAlexvenueno aff
Annie Chen, Lisa Taylor‐Swanson, Ronald W. Buie, Albert Park, Mike Conway

Bibliographic record

VenueInteractive Journal of Medical Research · 2018
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsnot available
FundersU.S. National Library of MedicineNational Institutes of HealthUniversity of WashingtonUniversity of Utah
KeywordsInformation retrievalHealth informationComputer scienceData scienceWorld Wide WebPsychologyPolitical scienceHealth care

Abstract

fetched live from OpenAlex

BACKGROUND: In recent years, there has been an increase in the utilization of complementary and integrative health (CIH) care, and an increase in information-seeking behavior focused on CIH. Thus, understanding the quality of CIH information that is available on the internet is imperative. Although there have been a limited number of studies evaluating the quality of websites providing information about specific CIH-related topics, a broad evaluation of CIH websites has not been conducted. OBJECTIVE: This study was designed to fill that gap. We set out to assess website quality in 5 CIH domains: (1) acupuncture, (2) homeopathy, (3) massage, (4) reiki, and (5) yoga. This study aimed to 1) characterize the websites by type and quality; 2) evaluate website characteristics which may affect readers' perceptions, specifically message content, structural features, and presentation style, and 3) investigate the extent to which harms, benefits and purposes of use are stated on websites. METHODS: This study employed a systematic search strategy to identify websites in each of the target domains to be evaluated. The websites were then classified by type, and a set of checklists focusing on quality, message content, structural features, and presentation style was used to evaluate the websites. Lastly, we performed content analysis to identify harms, benefits, and perceived purposes of use. RESULTS: There were similarities across domains regarding their overall quality and their message content. Across all domains, a high proportion of websites received strong scores in terms of ownership, currency, interactivity and navigability. Scores were more variable concerning authorship, balanced presentation of information and the use of sources of information. However, there were differences regarding their structural features and presentation style. Acupuncture and reiki sites tended to include more external links, and yoga, fewer. There was variation across domains in the extent to which the websites contained domain-specific terminology. Websites tended to provide an extensive list of potential benefits, while reporting of harms was scarce. CONCLUSIONS: This is the first study to perform a multidimensional assessment of websites in multiple CIH domains. This review showed that while there are similarities among websites of different CIH domains, there are also differences. The diverse distribution of website types suggests that, regardless of CIH domain, the public encounters information through many different types of media, and it would be useful to consider how the presentation of this content may differ depending on the medium. The characteristics for which variability exist are areas that warrant greater attention from researchers, policy makers, clinicians and patients. There is also a need to better understand how individuals may interact with CIH websites, and to develop tools to assist people to interpret the CIH-related information that they encounter.

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)0.014
metaresearch head score (Gemma)0.006
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 score0.443
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.179
GPT teacher head0.522
Teacher spread0.343 · 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

Citations20
Published2018
Admission routes1
Has abstractyes

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