Establishing research priorities for the Respiratory Therapy profession in Canada: A modified Delphi study
Bibliographic record
Abstract
Background: Respiratory therapists (RTs) play an important role in delivering care across diverse clinical settings. However, the research questions guiding the profession remain underdeveloped and often shaped by external professions. To address this gap, we conducted a national Delphi study to identify research priorities directly informed by the RT profession in Canada. Methods: We used a modified Delphi method, informed by a prior qualitative study, which aimed to identify and prioritize research needs across RT practice domains. We developed and distributed a list of 74 research items categorized into four domains to RTs across Canada through two rounds of online surveys. Consensus for items was defined as ≥70% agreement in Round 1 and ≥80% agreement in Round 2. Subgroup analyses were conducted by primary practice setting. Results: In Round 1, 286 participants reviewed 74 items and identified 11 (14.8%) as priorities. After incorporating open-ended feedback, some statements were combined, resulting in 53 statements carried forward. In Round 2, 165 participants reviewed 53 items and identified an additional 11 items (20.7%) as important. These topics reflect the breadth of RT practice, including airway management, rapid response teams, personalized ventilation strategies, and system-level contributions. Workforce sustainability issues such as burnout, staffing ratios, and retention were also identified. Subgroup analyses revealed meaningful variation in research priorities across different practice areas in Round 1 and 2. Conclusion: This study presents a profession-driven research agenda for respiratory therapy in Canada. These findings can inform future research, continuing professional development, and policy advocacy aligned with the profession's evolving roles and contexts.
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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 | 0.095 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".