Clinical Interdisciplinary Collaboration Models and Frameworks From Similarities to Differences: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: So far, various models of interdisciplinary collaboration in clinical nursing have been presented, however, yet a comprehensive model is not available. The purpose of this study is to review the evidences that had presented model or framework with qualitative approach about interdisciplinary collaboration in clinical nursing. METHODS: All the articles and theses published from 1990 to 10 June 2014 which in both English and Persian models or frameworks of clinicians had presented model or framework of clinical collaboration were searched using databases of Proquest, Scopus, pub Med, Science Direct, and Iranian databases of Sid, Magiran, and Iranmedex. In this review, for published articles and theses, keywords according with MESH such as nurse-physician relations, care team, collaboration, interdisciplinary relations and their Persian equivalents were used. RESULTS: In this study contexts, processes and outcomes of interdisciplinary collaboration as findings were extracted. One of the major components affecting on collaboration that most of the models had emphasized was background of collaboration. Most of studies suggested that the outcome of collaboration were improved care, doctors and nurses' satisfaction, controlling costs, reducing clinical errors and patient's safety. CONCLUSION: Models and frameworks had different structures, backgrounds, and conditions, but the outcomes were similar. Organizational structure, culture and social factors are important aspects of clinical collaboration. So it is necessary to improve the quality and effectiveness of clinical collaboration these factors to be considered.
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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 | 0.030 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".