Exploring knowledge gaps and research needs in respiratory therapy: A qualitative description study
Bibliographic record
Abstract
Background Respiratory therapists (RTs) are expected to stay updated on technology, treatments, research, and best practices to provide high-quality patient care. They must possess the skills to interpret, evaluate, and contribute to evidence-based practices. However, RTs often rely on research from other professions that may not fully address their specific needs, leading to insufficient guidance for their practice. Additionally, there has been no exploration of knowledge gaps and research needs from RTs’ perspectives to enhance their practice and patient outcomes. The research questions guiding this study were: ( i ) what are the perceived practice-oriented knowledge gaps? and ( ii ) what are the necessary research priorities across the respiratory therapy profession according to experts in respiratory therapy? Methods A qualitative description study was conducted using semi-structured focus groups with 40 expert RTs from seven areas of practice across Canada. Data was analyzed using qualitative content analysis. Results We identified four major themes relating to what these experts perceive as the practice-oriented gaps and necessary research priorities across the respiratory therapy profession: 1) system-level impact of RTs, 2) optimizing respiratory therapy practices, 3) scholarship on the respiratory therapy profession and 4) respiratory therapy education. Discussion The findings establish a fundamental understanding of the current gaps and the specific needs of RTs that require further investigation. Participants strongly emphasized the significance of research priorities that consider the breadth and depth of the respiratory therapy profession, which underscores the complex nature of respiratory therapy and its application in practice. Conclusion The unique insights garnered from this study highlight the knowledge gaps and research needs specific to RTs. These findings pave the way for further exploration, discourse, and research aimed at understanding the specific contributions and requirements of RTs.
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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 | 0.063 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".