The Science of Salt: A focused review on salt‐related knowledge, attitudes and behaviors, and gender differences
Bibliographic record
Abstract
The aim of the current review was to examine the scope of studies published in the Science of Salt Weekly that contained a measure of self-reported knowledge, attitudes, and behavior (KAB) concerning salt. Specific objectives were to examine how KAB measures are used to evaluate salt reduction intervention studies, the questionnaires used, and whether any gender differences exist in self-reported KAB. Studies were reviewed from the commencement of Science of Salt Weekly, June 2013 to the end of August 2017. Seventy-five studies had relevant measures of KAB and were included in this review, 13 of these were salt-reduction intervention-evaluation studies, with the remainder (62) being descriptive KAB studies. The KAB questionnaires used were specific to the populations studied, without evidence of a best practice measure. 40% of studies used KAB alone as the primary outcome measure; the remaining studies used more quantitative measures of salt intake such as 24-hour urine. Only half of the descriptive studies showed KAB outcomes disaggregated by gender, and of those, 73% showed women had more favorable KAB related to salt. None of the salt intervention-evaluation studies showed disaggregated KAB data. Therefore, it is likely important that evaluation studies disaggregate, and are appropriately powered to disaggregate all outcomes by gender to address potential disparities.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".