Bibliographic record
Abstract
Faced with soaring health-care costs, Canadian governments should consider creative ways to enable the population to stay healthy — and making it possible for Canadians to reduce their sodium intake is an extremely cost-effective way to do so. Excess sodium consumption is a risk factor for high blood pressure, stroke and heart disease. On average, Canadians consume 3,400 mg of sodium a day (1,100 mg over recommended levels), at least three-quarters of which comes from processed foods. Any attempt at sodium reduction must therefore involve the food industry. This paper surveys sodium reduction efforts in jurisdictions around the globe, as well as past Canadian attempts, to provide provincial and federal policymakers with a comprehensive suite of lessons learned and a host of far-sighted policy recommendations ranging from food procurement to legislation and private sector engagement. Provincial governments, individually or together, must launch multi-pronged efforts involving food service companies, manufacturers, post-secondary institutes and the media to ensure that low-sodium alternatives are readily available, and that consumers are aware of them. They must also support federal action on changing dietary guidelines and introducing restrictions on food advertising to children. The benefits to be had are very real. In light of evidence showing that population-level intervention is superior to clinical intervention in terms of cost-effectiveness, returning up to $11.10 for every dollar spent and generating tens of billions in direct health-care savings, there is a very strong case for investing in population-level sodium reduction interventions that will work. The time for governments to act is now.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".