Cancer Information Comprehension by English-as-a-Second-Language Immigrant Women
Bibliographic record
Abstract
Limited acculturation and socioeconomic factors have been associated with lower participation in cancer screening. Limited comprehension of cancer prevention information may contribute to this association. The authors used a stepwise linear regression to model acculturation and socioeconomic factors as predictors of comprehension (colon cancer and general health information) and screening intention in a sample of 78 Spanish-speaking immigrant women in Canada. The authors used the McNemar test to look for changes in women's screening intention. They used the Bidimensional Acculturation Scale, a language-based scale, to assess acculturation. Among English-as-a-second-language immigrant women, acculturation, television and Internet use, age, and Spanish-language education predicted comprehension of cancer prevention information, F(3, 69) = 6.76, p < .001, R(2) = .23. These variables also predicted comprehension of general health information, via the short form of the Test of Functional Health Literacy in Adults, F(4, 68) = 12.13, p < .001, R(2) = .42; and the Rapid Estimate of Adult Literacy in Medicine, F(2, 70) = 7.54, p = .001, R(2) = .17. However, the variables did not predict screening intention. More women expressed intention to be screened after reading the cancer prevention information than expected by chance alone, p = .002. Acculturation is an important influence on the comprehension of health information by older English-as-a-second-language immigrant women. However, other culture-related factors not measured by the Bidimensional Acculturation Scale likely influence their exposure to and understanding of health and cancer prevention information.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".