Endotracheal suctioning practices of nurses and respiratory therapists: How well do they align with clinical practice guidelines?
Bibliographic record
Abstract
BACKGROUND: A common procedure within intensive care units (ICUs) is the suctioning of respiratory secretions in patients who have been intubated or who have undergone tracheostomy. Previous studies have shown a wide variation in suctioning practices, and although current evidence does not support the routine practice of normal saline instillation (NSI), anecdotally, this is believed to be a common practice. OBJECTIVE: To examine the suctioning practices of registered nurses (RNs) and registered respiratory therapists (RRTs) in six hospital ICUs in Ontario, with special attention devoted to the use of NSI. METHODS: A 24-question, self-administered survey was distributed to 180 participants (90 RNs and 90 RRTs) working in the ICU of six hospitals in Ontario. The survey addressed individual suctioning practices within the ICU. RESULTS: The survey response rate was 96%. There were many similarities between the RRT and RN groups, with both reporting high use of NSI. Both groups observed side effects following NSI with suctioning including decreased oxygen saturation, patient agitation and increased volume of secretions. A significant number of participants from both the RN and RRT groups were unaware of the existence of suctioning and/or NSI protocols in the ICU. Some respondents reported that they routinely suctioned mechanically ventilated patients rather than as required. CONCLUSION: RNs and RRTs continue to practice NSI despite evidence-based practice guidelines suggesting that this therapy may be detrimental to patients. Increased awareness of best practices with respect to endotracheal tube suction generally, and NSI specifically, should be the focus of professional education in both groups of ICU staff.
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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 | 0.014 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".