18 Finding the place for nutrition in healthcare education and practice
Bibliographic record
Abstract
Background Malnutrition continues to impact healthcare outcomes, quality of life and costs to healthcare systems. Implementing nutritional care requires knowledge and skills which dietitians are trained for, however due to their limited numbers they rely on other healthcare professionals to recognise, initiate treatment, and subsequently refer where necessary. This paper describes an iterative development and implementation of nutrition medical education resources for doctors and healthcare professionals in England through a project called Nutrition Education Policy for Healthcare Practice. Method The interdisciplinary teaching team consisted of medical doctors, a registered dietitian, associate and registered nutritionists, a registered nurse, academic and education professionals. A two-stage process based on action research methodology was employed to develop and implement workshops. An initial pilot followed by 6 workshops reached 169 participants and delivered 13.5 hours of nutrition teaching. The workshops were evaluated using a combination of tools one designed by the NNEdPro team, others provided by the host organisations where the workshops were delivered. Further informal feedback during, and after, each road show was captured. Results Formal feedback on the workshops using the workshop evaluation tools was limited. A key finding from workshop delivery included lower attendance for voluntary workshops compared to mandatory workshops. Better reception of workshops which were delivered by doctors known to the participants and included local issues, and increased difficulty in organising interdisciplinary education due to low priority given to nutrition, and uncertainty of the professional roles in the delivery of nutrition care. Conclusion Although this project allowed successful development of resources for nutrition training of doctors and was successfully delivered and adapted, there was no clear “place” for this training in current healthcare teaching. One proposed way to change this is to demonstrate interprofessional roles through relevant clinical scenarios, aiming to align existing roles and workplace expectations as part of MDT, thus supporting dietitians in tackling malnutrition as a healthcare workforce.
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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 | 0.035 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".