Impact of team building using a novel technique (planned experimentation) to standardize endotracheal tube taping practice in the NICU
Bibliographic record
Abstract
Objectives: Convincing staff to standardize aspects of neonatal care is often difficult in the absence of strong evidence. A hospital-endorsed procedure for taping endotracheal tubes securely to a patient’s face lacked such evidence and as a result, many nurses in the neonatal intensive care unit continued to tape their own way. Multiple attempts to standardize this practice were unsuccessful. This report discusses how a staff centric team building management strategy combined with a planned experimentation exercise was able to raise compliance to 100%. Methods: A multidisciplinary team engaged staff in a planned experimentation exercise to rapidly study how endotracheal tubes are taped securely to an infant patient’s face. The exercise helped the team build trust between management and frontline staff. The exercise was a three month factorial study designed to identify a superior taping method among those already in practice. “Time to retaping” was the primary outcome that estimated how secure an endotracheal tube was. At the beginning of the study, the team acquired full staff commitment to standardize practice to whichever method the study favored, but if neither were better, staff agreed to follow the hospital endorsed method. Results: While a better method to secure endotracheal tubes was not identified, the hospital method of taping endotracheal tubes to an infant patient’s face was implemented into practice successfully. Conclusion: By establishing a clear commitment from staff to standardize based on results from the planned experimentation, the team was able to standardize endotracheal tube securement even while failing to define best practice.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".