Assessment of interprofessional collaborative practice components of perioperative teams
Bibliographic record
Abstract
Objective: The purpose of this study was to conduct a hospital workforce survey of nurses to determine what interprofessional collaborative practice components they have in place at their worksites. The findings could indicate what is needed to create, expand, and maintain an effective interprofessional collaborative practice environment.Methods: The study used a random sample of working perioperative nurses who were members of a national perioperative registered nurse association database and had interprofessional collaborative practice training either by continuing medical education or micro credentialing. These nurses were sent two surveys to assess their worksite presence of interprofessional components. These validated surveys assess an organization’s capacity to have an interprofessional collaboration by examining the workplace environment, environmental mechanism, and institutional support of interprofessional collaboration.Results: Interprofessional collaboration within the perioperative worksite setting exists in most of the structures in place. However, urban sites were more likely to lack supportive components that build, evaluate, and continuously create interprofessional teams.Conclusions: There was an uneven implementation of the interprofessional collaboration components. The components vary by site, with urban hospitals having few components resulting in a more asymmetrical interprofessional team. The study’s findings indicate a need for an assessment of worksite interprofessional collaboration to ensure all components are in place and for evaluation and improvement of interprofessional collaboration.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".