Influences of Public Health Policies on Individuals’ Dietary Behaviours: A Large Cohort Study from Chile
Bibliographic record
Abstract
ABSTRACT The Chilean law of food labeling and advertising was promulgated in 2012 and was implemented in consecutive phases until 2019. Aim: To determine the change in dietary behaviour experienced by the participants of a large Chilean cohort and to identify predictors of change. Methods: The sample included 2.608 adults between 35 and 70 years old recruited between 2006 and 2009 and followed on average over 10.8 years. Food intake was measured using a validated Food Frequency Questionnaire twice, at baseline, between June 2006 and October 2009, and after ten years of follow-up, between March 2018 and October 2019. The modified Alternative Healthy Eating Index (mAHEI) assessed participants’ diet quality. Also, other socio-demographic and health variables were measured. Results: During follow-up, the composition of the diet changed with an increase in the consumption of carbohydrates and fats and a decrease in the consumption of proteins. Also, 31.6% of participants improved their diet quality, but it worsened among 32.6% of participants. Being female, having a major health event, having a high educational level, and having sufficient household income were predictors for positive diet quality changes. Conclusions: During ten years of follow-up, the majority of participants did not improve their eating habits. Predictors of positive change were essentially the socio-demographic background and the occurrence of health events. Our findings suggest that it is necessary to reinforce policies related to diet with even more profound interventions than those already implemented.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".