Increased costs associated with greater adherence to the EAT-Lancet Commission reference diet in the province of Québec: the PREDISE Study
Bibliographic record
Abstract
Abstract The diet proposed by the EAT-Lancet Commission has faced criticism concerning its affordability. This study aimed to investigate the cost associated with a greater alignment to the EAT-Lancet reference diet in the province of Québec, Canada. The dietary habits of 1147 French-speaking adults were assessed using repeated web-based 24-h recall data collected between 2015 and 2017 in the cross-sectional PRÉDicteurs Individuels, Sociaux et Environnementaux (PREDISE) study. Diet costs were calculated using a Nielsen food price database. Usual dietary intakes and diet costs were estimated using the National Cancer Institute’s multivariate Markov Chain Monte Carlo method. Adherence to the EAT-Lancet diet was assessed using the EAT-Lancet dietary index (EAT-I). Associations between diet costs and EAT-I scores were evaluated using linear regression models with restricted cubic splines. After adjustment for energy intake, a higher EAT-I score (75th v. 25th percentiles) was associated with a 1·0 $CAD increase in daily diet costs (95 % CI, 0·7, 1·3). This increase in diet costs was mostly driven by the following component scores of the EAT-I (75th v. 25th percentiles, higher scores reflecting greater adherence): vegetables (1·6 $CAD/d, 95 % CI: 1·2, 2·1), free sugars (1·6 $CAD/d, 95 % CI: 1·3, 1·9), fish and plant-based proteins (1·4 $CAD/d, 95 % CI: 1·0, 1·8), fruits (0·9 $CAD/d, 95 % CI: 0·4, 1·3) and whole grains (0·4 $CAD/d, 95 % CI: 0·0, 0·8). Inversely, a greater score for the poultry and eggs component was associated with reduced diet costs (–1·2 $CAD/d, 95 % CI: −1·7, −0·7). This study suggests that adhering to the EAT-Lancet diet may be associated with an increase in diet costs in the province of Québec.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".