Bibliographic record
Abstract
This chapter decribes the narratives of individuals experiencing bullying in their workplace, significant to their understanding of solutions, perspectives, and attitudes toward bullying behaviors. Previous research outlined contributing factors-change in leadership, rigid bureaucracy, negative workplace culture pitting nurses against the nurse, loss of space, nursing the patients in hallways, and low healthcare quality linked to inadequate staffing. Develops a cost-benefit argument for organizations to deal with the issue and comments that despite the overwhelming benefits, few organizations appear to have workplace bullying on their agenda. Self-reported symptoms of healthcare professionals include depression, loneliness, isolation, and dread, as well as feelings of despair, helplessness, and job loss. The prior research's limitations prevent managers from finding effective solutions to the bullying problem. Bullying ought to be given the same legal standing as workplace violence and harassment. Nobody should have to endure the negative effects of being bullied at work by people of all ages. Findings suggest is no longer no longer fixed to older nurses eating their young, but the reversal is true of younger nurses skillfully suggesting that older nurses no longer belong in the workforce, asking them about their retirement plans. Future implications significant to this study are upcoming healthcare staffing.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".