Update and validation of the Canadian Behavior, Attitude and Nutrition Knowledge Survey (C-BANKS 2.0)
Bibliographic record
Abstract
Aim Understanding knowledge, attitudes and behaviors related to nutrition is crucial in developing effective intervention strategies to promote healthy eating habits. This study revised and updated the Canadian Behavior, Attitude and Nutrition Knowledge Survey (C-BANKS) to align with the current Canada’s Food Guide and dietary guidance and report on its reliability and validity with a new population. Method Data from 167 early childhood educators were recruited to test the revised C-BANKS’ reliability and validity. Reliability, as measured by internal consistency, was assessed using Cronbach’s alpha, while concurrent validity was determined by correlating the C-BANKS 2.0 scores with the CHEERS (Creating Healthy Eating and Active Environments Survey) and Mindful Eating Questionnaire (MEQ). Responsiveness was assessed by comparing a baseline C-BANKS 2.0 score to a post-test score after completing an online healthy eating and physical activity educational intervention. Results The adapted C-BANKS 2.0 questionnaire demonstrated good internal consistency (Cronbach’s alpha >0.70) and construct validity with related measures CHEERS and MEQ (p < 0.05). The C-BANKS 2.0 demonstrated strong responsiveness. Specifically, C-BANKS 2.0 scores increased after the nutrition education intervention (p < 0.001). Additionally, there were no signs of floor or ceiling effects. Conclusion The adapted C-BANKS 2.0 demonstrated satisfactory internal consistency, construct validity, and responsiveness to measure of nutrition knowledge, attitudes, and behaviors in an early childhood educator population. The revised C-BANKS 2.0 provides insight into the key factors that influence dietary habits thereby informing the design and evaluation of effective nutrition community intervention programs.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".