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Record W2120921634 · doi:10.1177/1524839910363536

Communicating During a Pandemic

2010· article· en· W2120921634 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHealth Promotion Practice · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsSimon Fraser UniversityCentre for Advancing Health OutcomesSt. Paul's Hospital
Fundersnot available
KeywordsPandemicPreparednessPublic relationsRelevance (law)Public healthMedicineHealth careFocus groupThe InternetInformation needsInformation DisseminationHealth communicationNursingCoronavirus disease 2019 (COVID-19)Internet privacyBusinessPolitical scienceMarketingDisease

Abstract

fetched live from OpenAlex

To prepare for pandemics, countries are creating pandemic preparedness plans. These plans frequently include crisis communication strategies that recommend conducting pre-crisis audience research to increase the effectiveness and relevance of communication with the public. To begin understanding the communication needs of the public and health care workers, 11 focus groups were conducted in Vancouver, Canada, in 2006 and 2007 to identify what information people want to receive and how they want to receive it. In the event of a pandemic, participants want to know their risk of infection and how sick they could become if infected. To make decisions about using vaccines and drugs, they want information that enables them to assess the risks of using the products. The public prefers to receive this information from family doctors, the Internet, and schools. Health care workers prefer to receive information in e-mails and in-services.

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.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.075
GPT teacher head0.437
Teacher spread0.362 · 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