Determining the Prevalence, Implementation Approaches, and Opinions of Above Cuff Vocalization: A Survey of Health Care Professionals
Bibliographic record
Abstract
OBJECTIVE: To conduct an international survey to investigate the use of above cuff vocalization (ACV) and how practice and opinion differs. DESIGN: Observational, cross-sectional online survey. SETTING: Critical care, acute, rehabilitation, long-term care, and community. PARTICIPANTS: Health care professionals involved in tracheostomy care or weaning (N=243). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Tracheostomy management, prevalence, personal experiences and opinions, and barriers to use. Quantitative data were reported descriptively, and content analysis was conducted with qualitative data. RESULTS: The survey was completed by 243 health care professionals from 9 professional groups and 25 countries, with most responses from the United Kingdom (54%) and speech and language therapists (55%). ACV was used in 39% of services (n=93). Sixty percent (n=50/83) of health care professionals with direct experience of ACV had used it with <10 people. Implementation of ACV varied widely concerning procedures, contraindications, safety processes, professionals involved, competencies, staff training, delivery, and outcome measures. The top benefits were communication (n=76/93; 82%), mood (n=62/93; 67%), and laryngeal sensation (n=49/93; 53%). Complications included discomfort (n=54/93; 58%) and strained vocal quality (n=39/93; 42%). Barriers to ACV implementation included lack of knowledgeable staff (n=92/238; 39%) and lack of access to training (n=73/238; 31%). CONCLUSIONS: ACV uptake varies internationally with no standardized approach to ACV delivery. Diversity of opinions on approaches and benefits exist. Serious complications are infrequent, but minor complications are common. Future research is needed to establish optimal ACV implementation to maximize benefits and minimize risks.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".