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Record W2135822740 · doi:10.1093/her/cyn008

Could pets be of help in achieving health literacy? A media analysis demonstration study

2008· article· en· W2135822740 on OpenAlexaffabout
Melanie Rock, Parabhdeep Lail

Bibliographic record

VenueHealth Education Research · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsHealth Sciences CentreSouth Health CampusUniversity of Calgary
Fundersnot available
KeywordsHealth literacyPsychologyMedia literacyLiteracyMedicineEnvironmental healthPedagogyPolitical scienceHealth care

Abstract

fetched live from OpenAlex

This paper asks whether, when seeking to reach the public, interest in the health of pets merits consideration. Our data set consisted of 128 items from Canadian media coverage, 1996-2006, that dealt with bovine spongiform encephalopathy (BSE) as well as with cats, dogs or both. Three main messages regarding pet health and human health were identified: 'do not worry', 'do worry' and 'be cautious'. A minority of articles did not convey a pet health message or a human health message (6%), and contradictory messages regarding human and animal health frequently occurred (32%). While we did not assess how members of the public actually received or interpreted these messages, media coverage dealing with pets does appear to have the potential to influence people. Media reports of British cats being harmed by BSE, in fact, may have influenced public views worldwide. Thus, professionals should give careful consideration to pets when conveying health information. Nevertheless, we do not suggest pet health information substitute for human health information. Rather, interest in pets may provide an opportunity to complement and to reinforce communication about human health.

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)0.002
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 score0.067
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.174
GPT teacher head0.569
Teacher spread0.395 · 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

Citations8
Published2008
Admission routes2
Has abstractyes

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