Conflict resolution strategies of nurses in a selected government tertiary hospital in the Kingdom of Saudi Arabia
Bibliographic record
Abstract
Background: Conflict is inevitable and can be found in all settings. It can co-exist between and among health care professionals such as doctors and nurses and their patients. The roles of the nurses in each scenario and the kind of strategies they utilized also vary. This study aimed to determine the conflict resolution strategies of nurses in a selected government tertiary hospital in the Kingdom of Saudi Arabia. Methods: Utilizing a Descriptive Correlational Research Design, 78 nurses were asked to identify their conflict resolution strategies during their day to day interaction with the patients and doctors through a 20-item questionnaire. This study was conducted in a government tertiary hospital specializing in maternity and pediatric care with 310 beds in the East of Riyadh. Results: Findings yielded a high utilization of conflict resolution strategies by nurses with patients. Accommodating (61.5%; n = 48) was ranked number one as nurses used this strategy in dealing with patients. Secondly, collaborative (60.3%; n = 47), the third is both compromising and avoiding at (57.7%; n = 45); and the least in rank is competing (56.4%; n = 44). Nurses utilized the following conflict resolution strategies with doctors such as: (1) competing (43.6%; n = 34), (2) both compromising and avoiding (42.3%; n = 33), (3) collaborative (39.7%; n = 31), and (4) accommodating (38.5%; n = 3). It shows that the number 1 priority for conflict resolution strategies is “accommodating” for patients which was regarded the least for the doctors. On the other hand, the least strategy “competing” with patients is the number 1 strategy of nurses with doctors. There is a significant relationship between nurses’ use of conflict resolution strategies consistent at collaborative with patients and doctors and their age. Findings further reveal that the overall use of conflict resolution strategies is significantly related to both patients and doctors. There is a significant relationship between nurses’ use of conflict resolution strategies at compromising with doctors and their nursing qualification. There is a significant relationship between nurses’ use of conflict resolution strategies (collaborative) with patients and their current nursing experience. The overall use of compromising as a strategy is significantly related to doctors. There is a significant positive correlation between the nurses’ scores of conflict resolution strategies for both patients and doctors. On the one hand, there is no significant difference relation between nurses’ use of conflict resolution strategies with patients and doctors and their socio-demographic variables (age, years of nursing experience) except nursing qualification. There is a significant difference between nurses’ use of conflict resolution strategies (avoiding) with patients and doctors and their qualifications. Conclusions: Consequently, conflict is inevitable and is still growing in healthcare. We have determined the importance of identifying the conflict resolution strategies being utilized by nurses when they deal with their patients and doctors. Nurses can safely identify conflict and implement systems for its management. Nurses and doctors must establish positive collegial relationships. The active management of conflict is an important aspect towards a positive collegial relationship. Doctors and nurses can effectively manage conflict to produce positive outcomes for patients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".