Bibliographic record
Abstract
In today's complex healthcare organizations, conflicts between physicians and nurses occur daily. Consequently, organizational conflict has grown into a major subfield of organizational behavior. Researchers have claimed that conflict has a beneficial effect on work group function and identified collaboration as one of the intervening variables that may explain the relationship between magnet hospitals and positive patient outcomes. The purpose of this study was to identify and compare conflict mode choices of physicians and head nurses in acute care hospitals and examine the relationship of conflict mode choices with their background characteristics. In a cross-sectional correlational study, 75 physicians and 54 head nurses in 5 hospitals were surveyed, using the Thomas-Kilmann Conflict Mode Instrument. No difference was found between physicians and nurses in choice of the most frequently used mode in conflict management. The compromising mode was found to be the significantly most commonly chosen mode (P = .00) by both. Collaborating was chosen significantly more frequently among head nurses (P = .001) and least frequently among physicians (P = .00). Most of the respondents' characteristics were not found to be correlated with mode choices. The findings indicate a need to enhance partnerships in the clinical environment to ensure quality patient care and staff satisfaction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.018 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".