A comprehensive systematic review of evidence on the structure, process, characteristics and composition of a nursing team that fosters a healthy work environment
Bibliographic record
Abstract
OBJECTIVES: The overall aim of this comprehensive systematic review was to identify the best available evidence on the effect of team characteristics, processes, structure and composition within the context of collaborative practice among nursing teams that create a healthy work environment. SEARCH STRATEGY: The search strategy sought to find both published and unpublished studies and papers written in the English language. An initial limited search of Medline and CINAHL databases was undertaken to identify optimal search terms. A second extensive search using all identified keywords and index terms was then undertaken. METHODOLOGICAL QUALITY: Two independent reviewers assessed the methodological quality of retrieved papers using the corresponding checklist from the System for the Unified Management, Assessment and Review of Information (SUMARI) package. RESULTS: The papers included in the review included nine experimental or quasi-experimental studies, 11 descriptive studies and four qualitative studies. A variety of different team structures such as interdisciplinary teams, primary nursing, team nursing, multidisciplinary models of care delivery and the use of a Partner in Patient Care model were investigated. Team characteristics should include accountability, commitment, enthusiasm and motivation. Social support within a team from a supervisor or colleague increased satisfaction levels of staff. CONCLUSION: The results of the review lead to the development of a number of recommendations for practice that could assist with creating a health work environment.
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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 | 0.024 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| 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".