Changing Sodium Knowledge, Attitudes and Intended Behaviours Using Web-Based Dietary Assessment Tools: A Proof-Of-Concept Study
Bibliographic record
Abstract
Despite public health efforts to reduce dietary sodium, sodium intakes in most countries remains high. The purpose of this study was to determine if using novel web-based tools that provide tailored feedback, the Sodium Calculator and Sodium Calculator Plus, improves users’ sodium-related knowledge, attitudes, and intended behaviours (KAB). In this single arm pre- and post-test study, 199 healthy adults aged 18–34 years completed a validated questionnaire to assess changes to sodium-related KAB before and after using the calculators. After using the calculators, the proportion of participants who accurately identified the sodium adequate intake and chronic disease risk reduction level increased (19% to 74% and 23% to 74%, respectively, both p = 0.021). The proportion accurately self-assessing their sodium intake as ‘high’ also increased (41% to 66%, p = 0.021). Several intended behavioural changes were reported, i.e., buying foods with sodium-reduced labels, using the Nutrition Facts table, using spices and herbs instead of salt, and limiting eating out. Evidence-based eHealth tools that assess and provide personalized feedback on sodium intake have the potential to aid in facilitating sodium reduction in individuals. This study is an important first step in evaluating and optimizing the implementation of eHealth tools to help reduce Canadians’ sodium intakes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".