All On One Page: Getting Daily Meal Ordering Right In A Rehabilitation Setting
Bibliographic record
Abstract
Abstract Background Recently the rehabilitation hospital expanded to over 100 beds providing improved access to rehabilitation for older persons in the area. During this time, an increase in incident report forms occurred due to the incorrect provision of diet/fluid to patients at ward level. In collaboration with relevant stakeholders, the Speech and Language Therapy (SLT) and Dietetic teams reviewed the current hospital meal ordering form and process in order to standardise a new system. The aim of this project was to reduce risk for patients and improve collaborative team working and communication. Methods A multidisciplinary team (MDT) approach was adapted involving; SLT, dietitians, catering department, nursing team and the patient quality and safety department. The HSE change framework provided scaffolding for this work. A new Daily Meal Order form was created and approved by the Nutrition Steering Committee and the process was agreed using input from all stakeholders. A pilot was carried out, reviewed and rolled out across the campus. The number of incidents recorded at ward level was used to measure if risk was reduced. A staff survey was conducted to gather feedback on the new system. Results The new Daily Meal Order form is now being used across the campus. Additional checks are completed each day via email to reduce the risk of errors. No incident reports were filed since launching the new form and process. A staff survey showed that all staff feel the new system and form constitutes an improvement, that it saves clinical time and that they would recommend it to others. Conclusion The project aims have been met in reducing patient risk, in line with the HIQA Safer Better Healthcare Programme. It has also improved MDT working and communication. This project is easily replicable to other settings and staff would recommend this approach to others.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".