An integrative review of the factors influencing new graduate nurse engagement in interprofessional collaboration
Bibliographic record
Abstract
AIM: To analyse critically the barriers and facilitators to new graduate nurse engagement in interprofessional collaboration. BACKGROUND: The acculturation of new graduate nurses must be considered in strategies that address the global nursing shortage. Interprofessional collaboration may support the transition and retention of new graduate nurses. DESIGN: Whittemore and Knafl's revised framework for integrative reviews guided the analysis. DATA SOURCES: A comprehensive multi-step search (published 2000-2012) of the North American interprofessional collaboration and new graduate literature indexed in the CINAHL, Proquest, Pubmed, PsychINFO and Cochrane databases was performed. A sample of 26 research and non-research reports met the inclusion criteria. REVIEW METHODS: All 26 articles were included in the review. A systematic and iterative approach was used to extract and reduce the data to draw conclusions. RESULTS: The analysis revealed several barriers and facilitators to new graduate engagement in interprofessional collaboration. These factors exist at the individual, team and organizational levels and are largely consistent with conceptual and empirical analyses of interprofessional collaboration conducted in other populations. However, knowledge and critical thinking emerged as factors not identified in previous analyses. CONCLUSION: Despite a weak-to-moderate literature sample, this review suggests implications for team and organizational development, education and research that may support new graduate nurse engagement in IPC.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".