Translational research to reduce trans-fat intakes in Northern Quebec (Nunavik) Inuit communities: a success story?
Bibliographic record
Abstract
Nunavik Inuit were thrice as highly exposed to dietary trans-fat as were Greenlandic Inuit, and that the biological levels found in Nunavik were already associated with deleterious blood lipid profiles, we decided to engage in translational activities. Our goal was to support Inuit communities in the practical implementation of a reduction of the trans-fat content of food sold in Nunavik. We carried out a preliminary feasibility study in Kuujjuaq and participated in several meetings. This translational phase involved an Inuk leader, an Inuk student, a southern student, a southern nutritionist and a southern researcher in the framework of a public health project. In the present article, we recount the different phases of the process, from research implementation to results dissemination and institutional commitment to implement a primary prevention program of reduction in trans-fat exposure in Nunavik. This is the occasion to draw broader conclusions on the factors that could either act in favour of or, on the contrary, would likely compromise the implementation of primary prevention interventions dealing with food and nutrition in the Arctic. Finally, we share some reflections on future translational activities dealing with trans-fat as well as other junk food issues. The analytical framework we propose integrates a range of factors, from geo-climatic to socio-economic, ethno-cultural, and even political, that we think should be examined while identifying and building preventive
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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 | 0.026 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".