Do not be salty: an analysis of consumers' salt reduction strategies investigated using word association tasks
Bibliographic record
Abstract
Summary Obesity and obesity‐related illness have become an increasingly prevalent problem in Canada and around the globe. Due to this situation, consumers' attitudes towards salt use have changed. The objective of this study was to investigate individuals' attitudes towards salt use and identify if they are choosing to reduce salt in their diet using unrestrictive questions. A convenience sample of 193 participants, residing in Atlantic Canada were recruited. Word association tasks were used to identify their attitudes towards salt‐reduced foods and high‐salt food products. Furthermore, an open‐ended comment question was used to identify consumers' salt reduction strategies. Participants considered salt‐reduced foods to be healthy and specifically indicated that they help to reduce blood pressure. However, they also considered them to be bland and lack flavour. The participants identified that they believed processed foods to be high in salt, especially processed meats. The majority of participants (70% of the overall population; 89% of older adults above 65 years of age) said they are trying to reduce their salt intake. The most frequently mentioned strategies for reducing salt and sodium intake were making homemade meals, purchasing low‐salt and sodium‐labelled foods, reading nutrition fact labels and removing saltshakers from their table at home.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".