<i>Canadian Nutrition Society</i> Dialogue on disease-related malnutrition: a commentary from the 2022 Food For Health Workshop
Bibliographic record
Abstract
This commentary represents a dialogue on key aspects of disease-related malnutrition (DRM) from leaders and experts from academia, health across disciplines, and several countries across the world. The dialogue illuminates the problem of DRM, what impact it has on outcomes, nutrition care as a human right, and practice, implementation, and policy approaches to address DRM. The dialogue allowed the germination of an idea to register a commitment through the Canadian Nutrition Society and the Canadian Malnutrition Task Force in the UN/WHO Decade of Action on Nutrition to advance policy-based approaches for DRM. This commitment was successfully registered in October 2022 and is entitled CAN DReaM (Creating Alliances Nationally for Policy in Disease-Related Malnutrition). This commitment details five goals that will be pursued in the Decade of Action on Nutrition. The intent of this commentary is to record the proceedings of the workshop as a stepping stone to establishing a policy-based approach to DRM that is relevant in Canada and abroad.
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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.035 | 0.021 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.057 | 0.055 |
| Insufficient payload (model declined to judge) | 0.008 | 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".