Standing up against workplace bullying behavior: Recommendations from newly licensed nurses
Bibliographic record
Abstract
OBJECTIVE: Workplace bullying exists in today's healthcare system and often targets newly licensed nurses. Experiences of workplace bullying behavior may negatively affect the nurses' physical and psychological health and impact job satisfaction and staff turnover rates at an organizational level. The purpose of this study was to explore strategies suggested by newly licensed nurses to prevent and intervene during incidents of workplace bullying behavior. METHODS: An exploratory qualitative design guided this study. Three open-ended questions asked included: What do you think could be done to prevent a future, similar incident of workplace bullying? If you or someone else attempted to the stop the bullying incident, please describe the actions taken. If you or someone else did not attempt to stop the bullying incident, please state what would need to happen for you to intervene on behalf of yourself or someone else. Surveys were distributed electronically to newly licensed nurses from three baccalaureate nursing programs who had participated in a workplace bullying education intervention study as students. A total of 79 responses were received. Responses to three open-ended questions about recent incidents of workplace bullying behavior were coded and analyzed. Then the Social-Ecological Model was used to organize results into individual, relationship, and organizational level strategies. RESULTS: Most respondents reported experiencing workplace bullying behaviors in the previous six months. Three domains of strategies were identified: Preventing Future Bullying Behavior, Stopping Incidents of Bullying Behavior, and Promoting Others to Act. CONCLUSIONS: Results indicated newly licensed nurses desire to be supported by their peers and organization as well as strategies to intervene when bullying behaviors occur. Implications for clinical practice and education are presented.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".