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Record W4238070818 · doi:10.32920/ryerson.14662722.v1

Usability of Health Promotional Materials and Their Translations by Newcomer Populations

2021· preprint· en· W4238070818 on OpenAlexaffabout
Uliana Roskina

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsToronto Metropolitan UniversityUniversity of Alberta
Fundersnot available
KeywordsReadabilityChristian ministryUsabilityImmigrationComprehensionHealth literacyReading (process)Health carePublic relationsReading comprehensionPopulationLiteracyPsychologyMedicinePolitical scienceComputer sciencePedagogyEnvironmental health

Abstract

fetched live from OpenAlex

Health promotional materials are used to increase people’s health literacy and educate them about important health related issues. However, research indicates that due to linguistic and cultural barriers, many immigrants do not comprehend the information provided in such materials. To reach the linguistically-diverse population of Ontario, the Ministry of Health and Long-Term Care prepares educational materials in different languages. However, no research has focused on immigrant reading comprehension of the health promotional materials. This research paper compares reading comprehension of health promotional materials in English and in Russian by Russian-speaking newcomers to examine the usability of the promotional materials on the Ministry of Health and Long-Term Care website. It then proposes policy recommendations to lower readability levels of the health promotional materials and their translations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.065
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.175
GPT teacher head0.488
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2021
Admission routes2
Has abstractyes

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