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Record W2994395820 · doi:10.1111/zph.12669

Lyme disease prevention: A content analysis of Canadian patient group and government websites

2019· article· en· W2994395820 on OpenAlexaffabout
Audrey‐Ann Journault, Lucie Richard, Cécile Aenishaenslin

Bibliographic record

VenueZoonoses and Public Health · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsUniversité de MontréalThe Quebec Population Health Research NetworkInstitut de recherche Robert-Sauvé en santé et en sécurité du travail
Fundersnot available
KeywordsGovernment (linguistics)Content analysisPublic healthMedicinePathology

Abstract

fetched live from OpenAlex

The quality of information presented on health-related websites-in terms of comprehensiveness, accuracy and consistency-is a public health concern. To date, the consistency of information across Canadian websites devoted to Lyme disease (LD) prevention has not been evaluated. The first aim of this study was to describe the contents and recommendations of LD prevention websites provided by two types of Canadian organizations: government (n = 3) and patient groups (n = 3). A second objective was to analyse the level of convergence among these websites in terms of their prevention-related content. Initial coding of the content resulted in information segments grouped into 114 subthemes related to nine overarching themes: tick habitat suitability, risk period, transmission, personal protection, peridomestic environmental management, tick identification and removal, early symptoms, testing and diagnosis and preventive treatment. Comparative content analyses were performed both within and between the content of websites of the two organization types. The themes most frequently addressed by both organization types were personal protection (20% of the prevention-related content in patient group websites and 22% of the prevention-related content in government websites), transmission (12% and 16%, respectively) and tick identification and removal (19% and 15%, respectively). Government websites' information was generally convergent with that of patient group websites (four highly convergent themes, three moderately convergent and two divergent). Nevertheless, of particular concern were divergent messages and inaccurate information found on 11 subthemes out of 103. Examples included other possible modes of transmission and the ineffectiveness of DEET insect repellent. These results suggest the need for public health and health communications research on the issue of the quality of LD prevention information found on the Internet.

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)0.000
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 score0.294
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.055
GPT teacher head0.292
Teacher spread0.236 · 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

Citations12
Published2019
Admission routes2
Has abstractyes

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