Managing and mitigating conflict in healthcare teams: an integrative review
Bibliographic record
Abstract
AIM: To review empirical studies examining antecedents (sources, causes, predictors) in the management and mitigation of interpersonal conflict. BACKGROUND: Providing quality care requires positive, collaborative working relationships among healthcare team members. In today's increasingly stress-laden work environments, such relationships can be threatened by interpersonal conflict. Identifying the underlying causes of conflict and choice of conflict management style will help practitioners, leaders and managers build an organizational culture that fosters collegiality and create the best possible environment to engage in effective conflict management. DESIGN: Integrative literature review. DATA SOURCES: CINAHL, MEDLINE, PsycINFO, Proquest ABI/Inform, Cochrane Library and Joanne Briggs Institute Library were searched for empirical studies published between 2002-May 2014. REVIEW METHODS: The review was informed by the approach of Whittemore and Knafl. Findings were extracted, critically examined and grouped into themes. RESULTS: Forty-four papers met the inclusion criteria. Several antecedents influence conflict and choice of conflict management style including individual characteristics, contextual factors and interpersonal conditions. Sources most frequently identified include lack of emotional intelligence, certain personality traits, poor work environment, role ambiguity, lack of support and poor communication. Very few published interventions were found. CONCLUSION: By synthesizing the knowledge and identifying antecedents, this review offers evidence to support recommendations on managing and mitigating conflict. As inevitable as conflict is, it is the responsibility of everyone to increase their own awareness, accountability and active participation in understanding conflict and minimizing it. Future research should investigate the testing of interventions to minimize these antecedents and, subsequently, reduce conflict.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".