Staffing patterns of respiratory therapists in critical care units of Canadian teaching hospitals.
Bibliographic record
Abstract
BACKGROUND: The optimal level of respiratory therapy staffing in Canadian intensive care units (ICUs) has not been described in the literature. An examination of practice patterns is an essential first step in developing an understanding of the contribution of respiratory therapists (RTs) to both short- and long-term patient outcomes in this context. OBJECTIVE: To identify the ratio of mechanically ventilated patients to respiratory therapist (Vent:RT ratio) in the ICUs of Canadian teaching hospitals and the factors that influence this ratio. METHODS: test. Approval for the study was granted by the University of Manitoba Research Ethics Board (Winnipeg, Manitoba). RESULTS: A mean (± SD) Vent:RT ratio of 5.1:1±2.818 was determined. Repeated measures ANOVA demonstrated no significant differences between findings of the three data collection points (F [1.7,30.5]=0.695; P=0.492). Several variables were associated with a significant difference in the Vent:RT ratio including ICUs where RTs insert arterial monitoring lines (4.05±2.89 versus 6.97±2.85; t[17.6]=-2.64; P=0.02), neurological ICUs (4.04±2.76 versus 6.40±3.35; t[30]=-2.092; P=0.04) and coronary care units (5.72±2.80 versus 3.10±1.88; t[35]=2.72; P=0.01). Significant differences were also identified in the mean number of RT hours worked in ICUs where RTs intubated (31.40±9.71 versus 60.54±47.20; t(13)=-2.17; P=0.049) and procured arterial blood gases (41.68±30.85 versus 77.33±46.22; t[35]=-2.79; P=0.01). CONCLUSIONS: The present study is the first to report the Vent:RT ratio and RT practice patterns in Canadian adult ICUs. The results serve as a baseline for comparison of staffing norms and will enlighten future research on the impact of RT staffing and practice patterns on patient outcomes.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".