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Record W2126209530 · doi:10.1093/heapro/dar019

Application of the health literacy framework to diet-related cancer prevention conversations of older immigrant women to Canada

2011· article· en· W2126209530 on OpenAlexafffundabout
Maria D. Thomson, Laurie Hoffman‐Goetz

Bibliographic record

VenueHealth Promotion International · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHealth literacyImmigrationLiteracyLanguage barrierHealth communicationHealth equityGerontologyCancer preventionHealth educationPsychologyMedicineNursingHealth carePedagogyCommunicationCancerPublic healthPolitical science

Abstract

fetched live from OpenAlex

Health literacy, conceptualized as a framework involving basic (functional), interactive and critical skill sets, is a key determinant of health. Application of the health literacy framework (HLF) to immigrant populations has been limited. Our objective was to apply the HLF to discourses about diet-related colon cancer prevention among English-as-a-Second-Language (ESL) immigrant women. We also explored whether these discussions could inform the development of culturally appropriate information and potentially increase health literacy. Interviews were conducted with 64 older Spanish-speaking ESL immigrant women. Directed content analysis guided by the HLF was used to identify themes. Diet-related conversations were initiated by 43 (67%) participants. Four themes were identified: general information requests-low functional health literacy (FHL) (n = 23/43), specific nutrition inquiries-high FHL (n = 17/43), actions for healthy eating-low interactive health literacy (IHL) (n = 8/43) and community communication issues-high IHL (n = 3/43). No conversations representing critical health literacy were identified. Five women discussed both FHL and IHL themes. Women's diet-related conversations followed a continuum of increasing information needs supporting the HLF.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.441
Teacher spread0.399 · 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.

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

Citations15
Published2011
Admission routes3
Has abstractyes

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