Conflicting Working Relationships Among Nurses: The Intersection of Should Nursing, Double Domination, and the Big Picture
Bibliographic record
Abstract
BACKGROUND: Research conducted on conflict between Registered Nurses (RNs) has established that it happens regularly within the hospital setting, that it adversely affects the health and well-being of RNs, impacts the effective functioning of the health care organization, and compromises quality patient care. In this article, the phrase conflicting working relationships (CWRs) is used to represent working relationships between RN peers that are non-collegial, uncaring, and non- supportive, and inclusive of the behaviours associated with incivility, horizontal violence, and bullying, among others. PURPOSE: To examine how nursing, including nursing knowledge and practice, is socially organized within the hospital setting and how this organization is linked to CWRs between RN peers. METHODS: Interviews were conducted with 17 RNs, followed by text analysis and mapping guided by institutional ethnography (IE) as the research methodology. RESULTS: The intersections between should nursing, double domination, and the big picture threads shows work environments where RNs struggle to provide appropriate care and conflict has become institutionalized. The intersection between threads can be used as caution areas for RNs and individuals in leadership positions to reflect on nursing practice when conflict is being experienced. CONCLUSIONS: The contextual variables surrounding professional nursing practice are very influential with respect to how RNs relate to each other. A new type of dialogue about the organization of nursing practice in the hospital setting is needed to support more relational practices between RNs.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.036 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".