Assessing adherence and exploring barriers to provision of prescribed texture modifications for dysphagia in a residential aged care facility in rural Australia
Bibliographic record
Abstract
Purpose Purpose: Between 55–65% of residents living in residential aged care facilities (RACFs) experience dysphagia and are prescribed texture-modified diets by a speech-language pathologist (SLP). The aim of this study was to assess current adherence to prescribed texture modification for people with dysphagia; and explore barriers to implementation in a rural aged care setting.Method Method: Meal texture audits (N = 42) were completed with residents with dysphagia in a rural RACF who were prescribed texture-modified diets or fluids by a SLP. Semi-structured focus groups were conducted with nursing and food preparation staff (N = 11) to identify barriers to implementation.Result Result: Mealtime texture audits identified that 54.8% (n = 23) of residents' food modification requirements were incorrectly documented in the manual entry database (kitchen form) and 64.3% (n = 27) of meal trays contained foods that did not meet residents' dysphagia management plans. Focus group data revealed seven main themes impacting on the ability of staff to implement prescribed texture-modified diets. Complicated processes and communication between nursing, food services and SLP staff were identified as major barriers. These were complicated further by time pressures experienced by staff as well as staffing issues, resourcing of the kitchen, accommodating individual dietary preferences and the variety/presentation of dietary options at the aged care facility.Conclusion: There was low adherence to SLP prescribed texture-modified diets and fluids in the participating rural RACF. This study identified major barriers to implementing SLP prescribed texture-modified diets including complicated processes, communication breakdowns, time pressures and limited staffing. Implementation of an online menu management system and regular dysphagia-specific training may address barriers to communication and complicated paper-based menu systems and should be a priority for health services to ensure adequate dysphagia management.
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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 | 0.004 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".