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Record W4382623887 · doi:10.3389/fcomm.2023.1006784

Evaluation of the readability, understandability, and actionability of COVID-19 public health messaging in Atlantic Canada

2023· article· en· W4382623887 on OpenAlexafffundabout
Katherine Kelly, Alyson Campbell, Anja Salijevic, Sarah Doak, Laurie Michael, William Montelpare

Bibliographic record

VenueFrontiers in Communication · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsUniversity of Prince Edward Island
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsReadabilityCLARITYMisinformationHealth communicationIndex (typography)Reading (process)InfographicMedical educationComputer sciencePsychologyGeographyWorld Wide WebMedicinePublic relationsPolitical scienceData mining

Abstract

fetched live from OpenAlex

Introduction Effective communication of COVID-19 information involves clear messaging to ensure that readers comprehend and can easily apply behavioral recommendations. This study evaluated the readability, understandability, and actionability of public health resources produced by the four provincial governments in Atlantic Canada (New Brunswick, Newfoundland and Labrador, Nova Scotia, and Prince Edward Island). Methods A total of 400 web-based resources were extracted in June 2022 and evaluated using the Flesch-Kincaid Grade Level, CDC Clear Communication Index, and the Patient and Education Materials Assessment Tool. Descriptive statistics and a comparison of mean scores were conducted across provinces and type of resources (e.g., text, video). Results Overall, readability of resources across the region exceeded recommendations, requiring an average Grade 11 reading level. Videos and short form communication resources, including infographics, were the most understandable and actionable. Mean scores across provinces differed significantly on each tool; Newfoundland and Labrador produced materials that were most readable, understandable, and actionable, followed by New Brunswick. Discussion Recommendations on improving clarity of COVID-19 resources are described. Careful consideration in the development of publicly available resources is necessary in supporting COVID-19 knowledge uptake, while reducing the prevalence of misinformation.

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)0.018
metaresearch head score (Gemma)0.007
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 score0.277
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.007
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)0.0000.000

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 head0.188
GPT teacher head0.408
Teacher spread0.220 · 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

Citations5
Published2023
Admission routes3
Has abstractyes

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