Knowledge of and Adherence to Fruit and Vegetable Recommendations and Intakes: Results of the 2003 Health Information National Trends Survey
Bibliographic record
Abstract
Attention to cancer-relevant communication (e.g., fruit/vegetable intake recommendations) through various media has been shown to be a pivotal step in reduction of the cancer burden, thus underscoring the importance of examining associations between exposure to health media and knowledge of and adherence to fruit/vegetable intake recommendations. The purpose of the present study was to assess factors associated with fruit/vegetable intake knowledge and behavior. The authors analyzed data collected from the 2003 Health Information National Trends Survey to evaluate the effect of fruit/vegetable intake knowledge on behavior, and the relationship of this effect with biobehavioral, sociodemographic, and communication characteristics. Participants who were knowledgeable of fruit/vegetable intake recommendations and consumed at least 5 fruit/vegetable servings per day were classified as informed compliers. Associations were observed for being an informed complier and paying "a lot" of attention to health media on the radio, in the newspaper, and in magazines and "a little" or "some" attention to health media in magazines or on the Internet. The recent explosion of available cancer-related information through various media underscores the importance of examining associations between exposure to health media and knowledge of and adherence to fruit/vegetable intake recommendations.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".