The Contributions of Respiratory Therapists in the United States of America: A Scoping Review.
Bibliographic record
Abstract
Respiratory therapists (RTs) provide essential care across the United States of America (USA) in various health care settings, with a prominent role in critical care. RTs specialize in managing mechanical ventilation, maintaining patent airways, and assisting with specialized procedures. While RTs in the USA have a broad scope of practice, it varies based on multiple factors; as a result, limited evidence exists detailing their specific roles and responsibilities. This review aims to map the breadth and depth of the available literature on RT practice in critical care within the USA. This review included three aims: (1) describe the practices and roles of RTs in adult critical care settings (including value-efficiency scores), (2) summarize the different RT models of care in critical care teams in the USA, and (3) investigate regional variations in RT practices in critical care across the USA. This scoping review was guided by the Joanna Briggs Institute methodology for scoping reviews. Studies that described the RT role in adult critical care within the USA were included. Data extraction was informed by the Respiratory Therapy Practice-Based Outcome Initiative (RT-PBOI) model and value-efficiency metrics. Eighty peer-reviewed articles and 45 pieces of grey literature met the inclusion criteria (total 125). RTs' roles and responsibilities were categorized per the RT-PBOI model as follows: Technical Skills (131), Approach to Practice (90), Leveraging Capacity (45), Strategic Expertise (73), and Growing Value for the Future (16). The grey literature (45) provided brief descriptions of the RT scope from various state regulatory/licensing bodies or state respiratory societies, guidelines, reports, and/or position statements. While RTs are crucial to critical care, gaps remain in quantifying their impact and clearly defining their evolving scope of practice. Further research is essential to optimize their integration into care teams, quantify impact, and improve patient outcomes.
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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 | 0.021 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| 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".