Research capacity of respiratory therapists: A survey of views, opinions and barriers.
Bibliographic record
Abstract
BACKGROUND: Evidence-based practice (EBP) is increasing in health care services. This means that respiratory therapists (RTs) should be effective consumers, users and producers of scientific research pertaining to respiratory therapy technology and respiratory physiology. However, little is known about RT opinions and attitudes toward research. Survey instruments to measure them are also uncommon. OBJECTIVE: The present article presents the results of a survey of RTs regarding research attitudes including interest, self-perceived skill and barriers. METHODS: A survey was developed in consultation with practicing RTs and education researchers. It was fielded in six academic hospitals in Toronto, Ontario. Surveys were completed and returned anonymously. Descriptive statistics and associations were examined. Subgroup differences were tested using ANOVA methods. RESULTS: Surveys were completed by 112 RTs (response rate 26.9%). The majority (approximately 80%) of respondents agreed that respiratory therapy research is important, that research can advance the profession and that RTs are suited to performing respiratory therapy research. More than 70% were interested in performing research as long as barriers were eliminated. Among eight potential barriers, lack of time was ranked as the top barrier 59% of the time. Lack of interest in performing research was the least relevant barrier. RTs' educational attainment was positively associated with willingness to perform research and belief in having the skills needed for research. CONCLUSION: Many RTs want to conduct research. They would need substantial support, including increased research exposure during respiratory therapy training, more time and support from trained researchers.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".