Development and Initial Reliability Testing of NAK-50+: A Nutrition Attitude and Knowledge Questionnaire for Adults 50+ Years of Age
Bibliographic record
Abstract
PURPOSE: Few questionnaires to test nutrition knowledge and attitudes of older adults living independently in the community have been developed and tested to assess self-management tools such as Nutri-eSCREEN and other education programs. This study is a first step in the development of a questionnaire designed to evaluate the nutrition knowledge and attitudes of independent older adults (NAK-50+). METHODS: The steps involved in this study were: (i) drafting initial questions based on the content of the Nutri-eSCREEN education material, (ii) using cognitive interviewing to determine if these questions were understandable and relevant (n = 9 adults ≥50 years of age), and (iii) completing test-retest reliability in a convenient community sample (n = 60 adults ≥50 years of age). Intra-class coefficients (ICC) and kappa were used to determine reliability. RESULTS: A 33-item questionnaire resulted from this development and analysis. ICC for the total score was 0.68 indicating good agreement and thus initial reliability. CONCLUSIONS: NAK-50+ is a face valid and reliable questionnaire that assesses nutrition knowledge and attitudes in independent adults aged ≥50 years. Further work to determine construct validity and to refine the questionnaire is warranted. Availability of the questionnaire for this age group will support rigorous evaluation of education and self-management interventions for this segment of the population.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".