An examination of Dietary Inflammatory Index scores in a Canadian nationally representative sample
Bibliographic record
Abstract
There is growing recognition that diet can influence an individual’s inflammation level and ultimately, their risk for developing disease (Minihane et al., 2015; Prasad et al., 2012). The Dietary Inflammatory Index (DII) was developed to assess the inflammatory potential of an individual’s diet and pro-inflammatory diets have been linked to numerous chronic diseases (Phillips et al., 2019). That said, there is a paucity of research examining the inflammatory nature of diets in Canada. The current project used data from the Canadian Community Health Survey – Nutrition 2015. The study sample consisted of adults aged 18-65, who were not pregnant. 24-hour dietary recalls were used to calculate DII and Health Eating Index – Canada (HEI-C) scores. Adherence to food guide recommendations was assessed using Health Canada’s four-tiered, grading system. The association between DII and HEI-C scores, as well as adherence to the Canadian Food Guide recommendations were measured. The influence of personal and external factors that influence diet and DII scores was examined using a structural equation modelling approach. Results indicated that there was an inverse relationship between adherence to food guide recommendations and DII scores; however, no significant relationship was observed for HEI-C scores. Higher DII scores were noted for individuals who were not married or had lower levels of education or income. The best fitting structural equation model included variables for sex, income, number of foods consumed and length of time since immigration. The findings in this thesis indicated that certain Canadian populations have significantly higher DII scores and this knowledge could lead to the development of effective interventions to promote the consumption of an anti-inflammatory diet.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".