Respiratory Therapists’ role in caring for ventilator assisted individuals in the community: A scoping review
Bibliographic record
Abstract
Rationale Optimal patient flow, which frees critical care beds for those most in need, is improved when ventilator-assisted individuals (VAIs) can efficiently transition to safe environments such as complex continuing care or the community. In Canada, clinicians work with respiratory therapists (RTs) to care for VAIs outside acute care, but information on their role and contributions in these settings is limited.Objectives The aims of this study were to describe the role and impact of RTs in caring for VAIs outside the acute care environment.Methods A scoping review was conducted to identify sources describing the RT role in managing VAIs outside acute care. The Respiratory Therapy Practice-Based Outcome Initiative (RT-PBOI) model with 5-domains was used to describe RT contributions to patient care and healthcare utilization.Measurements and Main Results A total of 14 peer-reviewed studies and 6 grey literature sources were included. Reported RT roles were in the care of VAIs requiring prolonged-, long-term- and home mechanical ventilation. Of the included studies, 12 reported Technical Skills, 11 Approaches to Practice, 3 Leveraging Capacity, 7 Strategic Expertise, and 1 Future Value. None of the grey literature addressed Strategic Expertise. Airway and mechanical ventilation management, patient education and discharge coordination were identified as key RT skills within the interprofessional team.Conclusions RTs are key facilitators in support of both VAIs and caregivers in the community, providing airway and mechanical ventilation, education and transition facilitation. Ongoing assessments of their role and contributions will enhance the needs of VAIs and their caregivers outside of acute care.
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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 | 0.020 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".