A nursing perspective of interprofessional work in critical care: Findings from a secondary analysis
Bibliographic record
Abstract
PURPOSE: This article presents a secondary analysis of nurse interviews from a 2-year comparative ethnographic study exploring cultures of collaboration across intensive care units (ICU). Critically ill patients rely on their interprofessional health care team to communicate and problem-solve quickly to give patients the best outcome available. Critical care nurses function at the hub of patient care giving them a distinct perspective of how interprofessional interactions impact collaborative practice. MATERIALS AND METHODS: Secondary analysis of a subset of primary qualitative data is appropriate when analysis extends rather than exceeds the primary study aim. Primary ethnographic data included 178 semistructured interviews of ICU professionals from 8 medical-surgical ICUs in North America; purposeful maximum variation sampling was used to represent each profession accurately. Fifteen anonymized ICU nurse interview transcripts were coded iteratively to identify emerging themes impacting interprofessional collaborative practice. RESULTS: Findings suggest that quality of interprofessional collaboration is a product of a multitude of factors occurring at multiple levels within the organization. Managerial and organizational factors related to ICU nurse training and staffing may impede development of nurses' interprofessional skills. CONCLUSION: Deliberative development of ICU nurses' interprofessional skills is essential if nursing is to move from primary coordinator to active collaborator in patient management.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".