Original Paper Survey of Nutrition Knowledge of Canadian Physicians
Bibliographic record
Abstract
Objectives: Previous reports have indicated that physicians generally have little training in nutrition and a poor knowledge of the subject. A survey was carried out to determine the nutrition knowledge of physicians working in general practice. Methods: A questionnaire with multiple-choice questions was mailed to 248 physicians working in Alberta, Canada, mainly in Edmonton and Calgary. Non-respondents received a second questionnaire and a phone call. Results: Completed questionnaires were received from 36.1 % (84 of 233 eligible physicians). The average correct response was 63.1%. The results indicate that physicians are generally aware of information which has been publicized in the medical press: which nutrients are antioxidants; the nutrient associated with the prevention of neural tube defects (folate); the preventive action of fruit and vegetables against cancer; the energy value of fat (9 kcals/g); and the recommended fat intake (under 30 % of energy). By contrast they have a poor knowledge of other important topics in nutrition: the typical salt intake of Canadians; the association between excess protein intake and calcium loss; the type of dietary fiber helpful in lowering the blood cholesterol level (soluble fiber); and the nutrient which helps prevent thrombosis (omega-3 fat). Conclusions: These results support other data that physicians need more training in nutrition.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".