442 SUCCESS OF UK POPULATION SALT REDUCTION
Bibliographic record
Abstract
Objective: The UK is reducing salt intake. We provide an insight into the programme with an aim of helping other countries to follow. Methods: The key to the UK policy is (1) Setting up action group with strong leadership; (2) Determining salt consumption by measuring 24-hour urinary sodium (UNa) in a random sample of the population, identifying the major sources of salt, and developing a salt reduction strategy; (3) Encouraging industry reformulation, through progressively lower voluntary salt targets for >80 categories of foods, with a clear timeframe; (4) Consumer awareness campaign; (5) Clear nutritional labelling of foods; (6) Monitoring progress by (a) frequent surveys of salt content in foods with naming and shaming as well as praising individual companies, (b) repeated 24-hour UNa at 2-3 year intervals. Findings: (1) Product surveys consistently demonstrate significant reductions in the salt content of foods, e.g. 20% reduction of the salt content in bread from 2001 to 2011. (2) The average salt intake in the population as measured by 24-hour UNa decreased from 9.5 g/d in 2001 to 8.1 g/d in 2011 (i.e. 15% reduction, P < 0.05). Conclusions: The UK salt reduction programme is successfully reducing the salt intake of the whole UK population by gradual reformulation on a voluntary basis. Several countries, e.g. the US, Canada and Australia, are following the UK's lead. The challenge now is to engage all other countries around the world with appropriate local modifications. A reduction in salt intake worldwide will result in major public health improvements and cost-savings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".