Exploring gaps, opportunities, barriers and enablers in malnutrition policy through key informant interviews: a qualitative inquiry from the CANDReaM initiative
Bibliographic record
Abstract
Objectives: Disease-related malnutrition (DRM) presents in up to half of adults and one-third of children admitted to Canadian hospitals and significantly impacts health outcomes. Strategies to screen, diagnose and treat DRM exist but policy to facilitate implementation and sustainability are lacking. The purpose of this study was to explore gaps, opportunities, barriers and enablers for DRM policy in Canada. Methods: A qualitative study was conducted with multi-national key informants in DRM and/or health policy. Purposive sampling identified participants for a semi-structured interview. The health policy triangle framework informs policy outcomes by considering actors, content, context and processes, and was used to guide this work. Inductive thematic analysis was completed, followed by deductive analysis based on the framework. Results: DRM policy actors were seen as champions in healthcare, senior leaders in healthcare administration and individuals with lived experience. Policy content focused on screening, diagnosis and treatment of DRM. Key areas related to policy context included system specifics related to setting, cost and capacity, and social determinants of health. DRM policy processes were viewed as cross-sectoral and multi-level governance, mandating and other reinforcement strategies, windows of opportunity, and evaluation and research. Conclusions: DRM care has advanced substantially, yet policy-level changes are sparse, and gaps exist. DRM policy is facilitated by similar content around the globe and needs to be tailored to address setting-specific needs. Actors, content, context and processes inform policy and can be a dominant lever to accelerate nutrition care best practices.
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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 | 0.024 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".