Development and validation of a nutrition knowledge questionnaire for a Canadian population
Bibliographic record
Abstract
OBJECTIVE: The present study aimed to develop and validate a nutrition knowledge questionnaire in a sample of French Canadians from the province of Quebec, taking into account dietary guidelines. DESIGN: A thirty-eight-item questionnaire was developed by the research team and evaluated for content validity by an expert panel, and then administered to respondents. Face validity and construct validity were measured in a pre-test. Exploratory factor analysis and covariance structure analysis were performed to verify the structure of the questionnaire and identify problematic items. Internal consistency and test-retest reliability were evaluated through a validation study. SETTING: Online survey. SUBJECTS: Six nutrition and psychology experts, fifteen registered dietitians (RD) and 180 lay people participated. RESULTS: Content validity evaluation resulted in the removal of two items and reformulation of one item. Following face validity, one item was reformulated. Construct validity was found to be adequate, with higher scores for RD v. non-RD (21·5 (sd 2·1) v. 15·7 (sd 3·0) out of 24, P<0·001). Exploratory factor analysis revealed that the questionnaire contained only one factor. Covariance structure analysis led to removal of sixteen items. Internal consistency for the overall questionnaire was adequate (Cronbach's α=0·73). Assessment of test-retest reliability resulted in significant associations for the total knowledge score (r=0·59, P<0·001). CONCLUSIONS: This nutrition knowledge questionnaire was found to be a suitable instrument which can be used to measure levels of nutrition knowledge in a Canadian population. It could also serve as a model for the development of similar instruments in other populations.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".