<i>Canadians’ Level of Confidence</i> in Their Sources of Nutrition Information
Bibliographic record
Abstract
North Americans have a great interest in health and nutrition. However, because sources of nutrition information are vast, the quality of the information varies. We explored the potential benefit of segmenting the Canadian population by geographical location and age in determining the principal sources of nutrition information and level of confidence in these sources. A survey was posted on the Dietitians of Canada website: subjects indicated how frequently they used different healthy eating sources and their level of confidence in the various sources. A total of 870 questionnaires were retained. Magazines, books, the Internet, food labels, and brochures were the most frequently used sources of information. Analyses indicated differences between geographic areas in the use of the media (radio, newspaper, television), dietitians, and naturopaths as sources of nutrition information (p<0.05), and between seven age groups (<18 to 65+ years) in the use of the media (p<0.05). Respondents reported being very confident about nutrition information received from dietitians, physicians, books, the government, and nurses, with some differences occurring between geographic areas (p<0.05).
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".