Underrepresentation of Respiratory Therapists as Experts in Delphi Studies on Respiratory Practices and Research Priorities
Bibliographic record
Abstract
Delphi survey techniques are a common consensus method used to collect feedback from an expert panel to inform practices, establish guidelines, and identify research priorities. Collecting respiratory therapists' (RT) expertise and experiences as part of consensus-building methodologies is one way to ensure that they align with RT practices and to better influence respiratory care practice. This narrative review aimed to report the RT representation in expert panels of Delphi studies focused on respiratory therapy practices and research priorities. The research question that guided this review is: to what extent are RTs included as expert participants among published Delphi studies relate to respiratory therapy and research topics? We conducted a structured search of the literature and identified 23 papers that reported Delphi studies related to respiratory care practices and 15 that reported on respiratory-related research priorities. Delphi studies that focused on reporting consensus on respiratory care practices included the following: (1) mechanical ventilation, (2) high-flow nasal cannula therapy, (3) COVID-19 respiratory management, (4) home oxygen therapy, (5) cardiopulmonary monitoring, and (6) disease-specific guidelines. Delphi studies that focused on establishing respiratory research priorities included the following: (1) theory and practice-orientated knowledge gaps, and (2) priority research topics for empirical investigation. The results of this review suggest that RTs were rarely included as expert participants and, when involved, were minimally represented (5% to 33%). Given RTs' diverse and relevant experience in respiratory care, incorporating their perspectives to inform future education, respiratory care practices, and research priorities would allow evidence to better align with knowledge gaps deemed important for the respiratory therapy profession.
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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 | 0.395 | 0.545 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".