Examination of food industry progress in reducing the sodium content of packaged foods in Canada: 2010 to 2013
Bibliographic record
Abstract
In 2010, as part of a national sodium reduction strategy, Canada published sodium reduction benchmark targets for packaged foods; however, no evaluation of this policy has occurred. The objective was to evaluate changes in the sodium content of packaged foods, identify categories reduced in sodium, and determine the proportion meeting Health Canada's sodium reduction benchmarks. This was a cross-sectional analysis of Canadian packaged foods in 2010 and 2013 (n = 10 487 and n = 15 394, respectively). Sodium content was obtained from the Nutrition Facts table. Overall, 16.2% of food categories had significantly reduced sodium levels. The greatest shifts in the distribution of sodium within food categories occurred in imitation seafood (mean ± SD, mg/100 g; 602 ± 50 to 444 ± 81, 26.2%, p = 0.002), condiments (1309 ± 790 to 1048 ± 620, 19.9%, p = 0.005), breakfast cereals (375 ± 26 to 301 ± 242, 19.7%, p = 0.001), canned vegetables/legumes (269 ± 156 to 217 ± 180, 19.3%, p < 0.001), plain chips (462 ± 196 to 376 ± 198, 18.6% p = 0.004), hot cereals (453 ± 141 to 385 ± 155, 15.0%, p = 0.011), meat analogues (612 ± 226 to 524 ± 177, 14.4%, p = 0.003), canned condensed soup (291 ± 62 to 250 ± 57, 14.1%, p = 0.003), and sausages and wieners (912 ± 219 to 814 ± 195, 10.7%, p = 0.012). The proportion of foods meeting at least 1 of the 3 phases of the sodium reduction benchmark targets slightly increased (51.4% to 58.2%) and the proportion exceeding maximum benchmark levels decreased (25.2% to 20.8%). These data provide a critical evaluation of changes in sodium levels in the Canadian food supply. Although progress in reducing sodium in packaged foods is evident, the food industry needs to continue efforts in reducing the sodium in the foods they produce.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".