<i>What Do Adults in Prince Edward Island</i>: Know About Nutrition?
Bibliographic record
Abstract
PURPOSE: To assess adults' knowledge of dietary recommendations, food sources of key nutrients, food choices, and diet-disease relationships. METHODS: A previously validated survey, designed to assess nutrition knowledge, was adapted for use in Prince Edward Island and mailed to a random sample of 3,500 adults (aged 18 to 74). Dillman's Total Design Method was followed and a response rate of 26.4% achieved. Mean scores and 95% confidence intervals (CIs) were calculated for the overall survey and for each section. Demographic variations were assessed by univariate analysis. RESULTS: Of an overall possible score of 110 points, the mean score with 95% CI was 71.0 (70.1, 71.9). Respondents scored higher on the sections on dietary recommendations, food sources, and food choices than diet-disease relationships. Demographic differences existed in gender, age, education, and income. Findings suggest that adults have good general knowledge of dietary recommendations, but lack knowledge about how to make healthier food choices and the impact of diet on disease risk. CONCLUSION: When designing intervention strategies, dietitians should consider targeted messages to provide adults with the information they need to make healthy food choices.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".