Cancer-malnutrition care beyond the hospital walls: a survey of nutrition governance, practice, knowledge and education needs in the primary care and community sector in Australia
Bibliographic record
Abstract
Abstract Purpose Cancer-related malnutrition remains a prevalent issue in cancer survivorship. This study aimed to understand cancer-related malnutrition practice, knowledge and education needs in the primary care and community sectors, as well as the transition of nutrition care from hospital cancer services following cancer treatment. Methods A cross-sectional survey was conducted with general practitioners (GPs), general practice nurses (GPNs) and dietitians in hospital cancer services, community services and primary care in Victoria, Australia. Results The 184 respondents represented dietitians (n = 152), GPs (n = 22) and GPNs (n = 10). GPs and GPNs, and dietitians working in primary care and community settings (78% and 63%, respectively) agree cancer malnutrition is going unrecognised in their service. Only 26% of community health services complete malnutrition risk screening and 35% of GPs and GPNs frequently weigh their patients with cancer. Most GPs and GPNs (88%) believe responsibility for malnutrition risk screening should be shared across disciplines and 94% would like access to a malnutrition screening tool. Only 32% of hospital oncology dietitians and 44% of GPs and GPNs frequently refer their patients to primary care or community dietitians. Conclusion Implementation of routine malnutrition risk screening in primary care and community services and improved transition of nutrition care between hospital, primary care and community practitioners is required. Targeted cancer malnutrition education and resources across all health sectors is warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".