Health policy to address disease-related malnutrition: a scoping review
Bibliographic record
Abstract
Background: Health policies promote optimal care, yet policies that address disease-related malnutrition (DRM) are lacking. The purpose of this study was to conduct a scoping review to identify literature on existing and planned policy to address DRM in children or adults and explore the settings, contexts and actors of DRM policy. Methods: A search strategy comprising DRM and policy keywords was applied to eight databases on 24 February 2023. Articles that addressed DRM and policy were selected for inclusion after two independent reviews. The health policy triangle (HPT) framework (ie, actors, content, contexts and processes considerations for policy) guided data extraction and thematic analysis. Results: A total of 67 articles were included out of the 37 196 identified. Some articles (n=14) explored established policies at the local level related to food and mealtime, nutrition care practices, oral nutritional supplement prescribing or reimbursement. Other articles gave direction or rationale for DRM policy. As part of the HPT, actors included researchers, advocacy groups and DRM champions while content pertained to standard processes for nutrition care such as screening, assessment, intervention and monitoring. Contexts included acute care and care home settings with a focus on paediatrics, adults, older adults. Processes identified were varied and influenced by the type of policy (eg, local, national, international) and its goal (eg, advocating, developing, implementing). Discussion: There is a paucity of global DRM policy. Nutrition screening, assessment, intervention and monitoring are consistently identified as important to DRM policy. Decision makers are important actors and should consider context, content and processes to develop and mobilise DRM policy to improve nutrition care. Future efforts need to prioritise the development and implementation of policies addressing DRM.
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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 | 0.041 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.028 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".