Application of the health literacy framework to diet-related cancer prevention conversations of older immigrant women to Canada
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".