Updating the Evidence: Suctioning Practices of Physiotherapists in Ontario
Bibliographic record
Abstract
Purpose: The purpose of this article is to describe current tracheal suctioning practices of physiotherapists in the province of Ontario and to determine what factors influence these practices. Method: A cross-sectional online survey was conducted. An online survey link and quick response code were mailed to Ontario physiotherapists who were actively providing patient care and were authorized to perform tracheal suctioning as identified by the College of Physiotherapists of Ontario. Results: Ninety physiotherapists participated in the survey (23% response rate). Most (66%) suctioned in an intensive care setting, and many (41%) reported frequently using a closed endotracheal suctioning system. Hyperoxygenation was frequently performed before suctioning by 48% of participants, and only 18% frequently hyperoxygenated after suctioning. Most participants reported infrequently performing saline instillation (52%) and infrequently hyperinflating before suctioning (79%). Clean gloves were reported as the personal protective equipment most frequently worn across all suctioning approaches, and goggles and sterile gloves were least often worn while suctioning. Previous suctioning experience had the most influence on suctioning practices, and limited access to equipment had the least influence. Conclusions: Some of the tracheal suctioning practices of physiotherapists in Ontario vary from evidence-based clinical guidelines.
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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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".