Barriers to Respiratory Care Research in the United States
Bibliographic record
Abstract
BACKGROUND: Respiratory therapists (RTs) are in a unique position to positively impact patient outcomes through respiratory care research. Research plays a key role in evidence-based medicine; however, few RTs perform and publish research. Identification of barriers experienced by RTs may help increase RT-driven research. Thus, we aimed to identify barriers and research interests for RTs. METHODS: American Association for Respiratory Care (AARC) members were invited to anonymously complete a survey via an electronic link posted on AARC Connect. Survey domains included research training, experience, reasons for doing research, important respiratory topics, and barriers to conduct research. RESULTS: Responses from 82 surveys were analyzed. The majority were female (56%), and most had a graduate degree (61%), with a mean working experience of 25.3 ± 13.6 y. Fifty-seven percent of respondents reported at least one publication in a peer-reviewed journal. The desire to improve patient outcomes was the top-ranked reason for doing research. Most received research training through a graduate-level program (56%), but few had a formal research mentor (26%). Clinical research (67%) and quality improvement (63%) were the most common types of research. Data collection was the most common research role (51%). Invasive ventilation, advanced monitoring, and airway clearance were identified as the most important research topics. The primary barriers for RTs to conduct research were lack of protected time for research, opportunities to participate, training, departmental support, and mentorship. CONCLUSIONS: Lack of time, resources, and opportunities were identified as the primary barriers to RT research, and many RTs have not received formal research training. Resources such as formal mentorship, funding, and protected time may help increase RT participation in research.
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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 | 0.057 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".