Health Literacy and Numeracy: Key Factors in Cancer Risk Comprehension
Bibliographic record
Abstract
In this age of chronic disease and shared decision making, individuals are encouraged to contribute to decisions about health care. Health literacy, including numeracy, is requisite to meaningful participation and has been accepted as a determinant of health. The purpose of this study was to describe the influence of literacy, consisting of prose and numeracy skill, math anxiety, attained education and context of information on participant ability to comprehend Internet-based colorectal cancer prevention information. Prose, numeracy, and math-anxiety data, as well as demographic details, were collected for 140 Canadian adults, aged 50 + years. Participants had adequate prose literacy (STOFHLA) scores, high STOFHLA numeracy scores, moderate levels of health-context numeracy, poorer general-context numeracy and moderate math anxiety. There was better comprehension by participants of common (9.14/11) compared with uncommon (7.64/11) colorectal cancer information (p < 0.01). Prose literacy, numeracy, math anxiety and attained education accounted for 60% of the variation in participant comprehension scores. Numeracy, ranging from basic to advanced proficiency, is required to understand online cancer risk information. Prose literacy enhances numeracy when the subject matter is less familiar. These findings highlight the importance of presenting Web-based information that accommodates diverse health literacy and numeracy levels.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".