NutriQuébec: a unique web-based prospective cohort study to monitor the population’s eating and other lifestyle behaviours in the province of Québec
Bibliographic record
Abstract
INTRODUCTION: The epidemic of non-communicable diseases including cardiovascular diseases and type 2 diabetes is attributable in large part to unhealthy eating and physical inactivity. In the fall of 2016, the Québec government launched its first-ever Government Health Prevention Policy (Politique gouvernementale de prévention en santé (PGPS)) to influence factors that lead to improved health status and quality of life as well as reduced social inequalities in health in the population of Québec. NutriQuébec is a web-based prospective open cohort study whose primary aim is to provide essential data for the evaluation of the PGPS on the Québec population's eating and other lifestyle behaviours over time. METHODS AND ANALYSIS: Over a first phase of 3 years, NutriQuébec will enrol 20 000 adults living in the province of Québec in Canada through a multimedia campaign designed to reach different segments of the population, including subgroups with lower socioeconomic status. Participants will be invited to complete on a web platform nine core questionnaires on a yearly basis. Questionnaires will assess several dimensions related to lifestyle, including eating and physical activity behaviours, as well as a large number of personal characteristics and global health status. Temporal trends in eating and lifestyle behaviours will be analysed in relation to the implementation of the PGPS to provide essential data for its evaluation at a population level. Data analyses will use sociodemographic weights to adjust responses of participants to achieve, so far as is possible, representativeness of the adult Québec population. ETHICS AND DISSEMINATION: Université Laval Research Ethics Board approved the NutriQuébec project. Data analysis, presentations in conferences and publication of manuscripts are scheduled to start in 2020. TRIAL REGISTRATION NUMBER: NCT04140071.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".