Ethnographic Investigation of Oral Care in the Intensive Care Unit
Bibliographic record
Abstract
BACKGROUND: Oral care plays a clear and important role in the prevention of ventilator-associated pneumonia. However, few studies have explored the actual work of oral care by nurses in the intensive care unit. OBJECTIVE: To explore intensive care nurses' knowledge of and experiences with the delivery of oral care to reveal less visible aspects of this work. METHODS: In an institutional ethnography, go-along and semistructured interview methods were used to explore the oral care practices and perspectives of 12 bedside nurses and 12 interprofessional (intensivist, allied health, and management) participants in an intensive care unit at a large urban teaching hospital in Ontario, Canada. RESULTS: Nurses described how obstacles frequently inhibited the delivery of oral care. Technical barriers included oral crowding with tubes and aversive responses by patients, such as biting. Contextual impediments to oral care included time constraints, lack of training, and limited opportunities for interprofessional collaboration. A key discovery was the presence of an informal unit-based nursing curriculum, whereby nurses acquired strategies to overcome barriers to oral care. Although the nurses did extensive problem solving in providing oral care, the interprofessional participants had limited knowledge of how oral care was accomplished. CONCLUSION: These data suggest the complexity of performing oral care in intensive care is underestimated and perhaps undervalued. Future research is needed to address technical and contextual barriers to optimize current guideline expectations for the provision of regular and effective oral care.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".