Transdisciplinary Screening and Intervention for Nutrition, Swallowing, Cognition and Communication: A Case Study
Bibliographic record
Abstract
Background: Transdisciplinary health research and clinical practice is supported by numerous Australian health workforce documents and the broader transdisciplinary research literature. This research assessed the impact of early screening and limited intervention for nutrition, swallowing, cognition, and communication deficits in medical admissions in a large metropolitan hospital. Methods and Findings: Validated screening tools were selected and consensus for interventions were obtained by dietetic and speech pathology disciplines. Intensive training was undertaken to familiarize staff members with the screening documents and project scope. Ethics approval was obtained. Participants included 179 patients aged ≥65 years admitted to the emergency department or medical unit. The project significantly reduced referral time to both disciplines, and time to full assessment in dietetics but not speech pathology. Results found 43% of patients were malnourished or at risk of malnutrition, and 14 patients had oral intake ceased due to swallowing difficulties.Conclusions: This study has demonstrated that transdisciplinary screening and intervention may work within dietetics and speech pathology, providing an innovative extension to practice. Further alternatives using this model include the use of allied health assistants or less-experienced clinicians, while opportunities exist for transdisciplinary practices within other healthcare disciplines.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".