The impact of organisational factors on horizontal bullying and turnover intentions in the nursing workplace
Bibliographic record
Abstract
AIM: To examine the impact of organisational factors on bullying among peers (i.e. horizontal) and its effect on turnover intentions among Canadian registered nurses (RNs). BACKGROUND: Bullying among nurses is an international problem. Few studies have examined factors specific to nursing work environments that may increase exposure to bullying. METHODS: An Australian model of nurse bullying was tested among Canadian registered nurse coworkers using a web-based survey (n = 103). Three factors - misuse of organisational processes/procedures, organisational tolerance and reward of bullying, and informal organisational alliances - were examined as predictors of horizontal bullying, which in turn was examined as a predictor of turnover intentions. The construct validity of model measures was explored. RESULTS: Informal organisational alliances and misuse of organisational processes/procedures predicted increased horizontal bullying that, in turn, predicted increased turnover intentions. Construct validity of model measures was supported. CONCLUSION: Negative informal alliances and misuse of organisational processes are antecedents to bullying, which adversely affects employment relationship stability. IMPLICATIONS FOR NURSING MANAGEMENT: The results suggest that reforming flawed organisational processes that contribute to registered nurses' bullying experiences may help to reduce chronically high turnover. Nurse leaders and managers need to create workplace processes that foster positive networks, fairness and respect through more transparent and accountable practices.
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".