Exploring augmentative and alternative communication in intensive care units: nurses’ experiences, knowledge, and training preferences in Cyprus
Bibliographic record
Abstract
PURPOSE: Nurses provide quality and safe care to critically ill patients in intensive care units (ICUs) who might also experience severe speech-language production and/or comprehension impairments. However, interpreting patients' communication efforts is not always possible for several reasons, such as lack of time or training. The current study aimed to investigate ICU nurses' Augmentative and Alternative Communication (AAC) knowledge and determine their preference for receiving training on AAC forms of communication. MATERIALS AND METHODS: One hundred and two ICU nurses from Cyprus's three largest public ICUs completed an electronic questionnaire with multiple-choice questions. RESULTS: The findings showed that participants use both unaided and aided forms of communication. The aided forms are limited to pen and paper and whiteboards, with the use of other assistive technology being scarce. It was also evident that participants had received minimal training in AAC. However, the nurses expressed a willingness to receive training in AAC to communicate with their patients. CONCLUSIONS: It is essential that AAC training is provided to ICU nurses during their academic training and as part of continuous professional education. Additionally, interdisciplinary collaboration with healthcare professionals, such as speech and language therapists specializing in ICU communication enhancement, is advised.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".