Delivering Interprofessional Care in Intensive Care: A Scoping Review of Ethnographic Studies
Bibliographic record
Abstract
BACKGROUND: The sustained clinical and policy interest in the United States and worldwide in quality and safety activities initiated by the release of To Err Is Human has resulted in some high-profile successes and much disappointment. Despite the energy and good intentions poured into developing new protocols and redesigning technical systems, successes have been few and far between, leading some to argue that more attention should be given to the context of care. OBJECTIVE: To examine the insights provided by qualitative studies of interprofessional care delivery in intensive care. METHODS: A total of 532 article abstracts were reviewed. Of these, 24 met the inclusion criteria. RESULTS: Articles focused on the nurse-physician relationship, patient safety, patients' families and end-of-life care, and learning and cognition. The findings indicated the complexities and nuances of interprofessional life in intensive care and also that much needs to be learned about team processes. CONCLUSION: The fundamental insight that interprofessional interactions in intensive care do not happen in a historical, social, and technological vacuum must be brought to bear on future research in intensive care if patient safety and quality of care are to be improved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".