Supporting nutrition in frail older people: a qualitative study exploring views of primary care and community health professionals
Bibliographic record
Abstract
BACKGROUND: Malnutrition is associated with increased morbidity and mortality, and is very common in frail older people. However, little is known about how weight loss in frail older people can be managed in primary care. AIMS: To explore the views and practices of primary care and community professionals on the management of malnutrition in frail older people; identify components of potential primary care-based interventions for this group; and identify training and support required to deliver such interventions. DESIGN AND SETTING: Qualitative study in primary care and community settings. METHOD: = 60 participants). Data were analysed using thematic analysis. RESULTS: Primary care and community health professionals perceived malnutrition as a multifaceted problem. There was an agreement that there is a gap in care provided for malnutrition in the community. However, there were conflicting views regarding professional accountability. Challenges commonly reported by primary care professionals included overwhelming workload and lack of training in nutrition. Community MDT professionals and dietitians thought that an intervention to tackle malnutrition would be best placed in primary care and suggested opportunistic screening interventions. Education was an essential part of any intervention, complemented by social, emotional, and/or practical support for frailer or socially isolated older people. CONCLUSIONS: Future interventions should include a multifaceted approach. Education tailored to the needs of older people, carers, and healthcare professionals is a necessary component of any intervention.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".