Antecedents and consequences of intra-group conflict among nurses
Bibliographic record
Abstract
AIM: To test a theoretical model linking selected antecedent variables to intra-group conflict among nurses, and subsequently conflict management style, job stress and job satisfaction. BACKGROUND: A contributing factor to the nursing shortage is job dissatisfaction as a result of conflict among nurses. To develop strategies to reduce conflict, research is needed to understand the causes and outcomes of conflict in nursing work environments. METHOD: A predictive, non-experimental design was used in a random sample of 277 acute care nurses. Structural equation modelling was used to analyse the hypothesised model. RESULTS: Nurses' core self-evaluations, complexity of care and relationships with managers and nursing colleagues influenced their perceived level of conflict. Conflict management style partially mediated the relationship between conflict and job satisfaction. Job stress had a direct effect on job satisfaction and core self-evaluation had a direct effect on job stress. CONCLUSION: Conflict and its associated outcomes is a complex process, affected by dispositional, contextual and interpersonal factors. How nurses manage conflict may not prevent the negative effects of conflict, however, learning to manage conflict using collaboration and accommodation may help nurses experience greater job satisfaction. IMPLICATIONS FOR NURSING MANAGEMENT: Strategies to manage and reduce conflict include building interactional justice practices and positive interpersonal relationships.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".