Bibliographic record
Abstract
Canadian frontline nursing managers are observing an increase in the reporting of workplace bullying as more nurses become aware of their employers' legal obligations to provide employees with a respectful workplace, per the Canada Human Rights Code, Canada Labor Code, and Canada Occupational Health and Safety Regulations. One problem with this reporting is that the victim's reports of bullying may become overshadowed by the bully's reports of victim incompetence, resulting in the victim experiencing further victimization. Bullies may report the victim (target) as inept, deficient in knowledge, or lacking ability. Fear of re-victimization plays a significant role in the victim's failure to report workplace bullying. It is important that managers focus on the bullying and not on the perceived character flaws described by the bully. The author begins by describing workplace bullying and reviewing the workplace bullying literature. She then presents and discusses a composite case study. To assist managers in discouraging bullying she shares supports for addressing bullying, specifically workplace policies, collective agreements, human resources departments, mediation, alternative dispute resolution, and arbitration, and concludes by reminding frontline managers of their important role in identifying bullying and understanding the victim's fears of further victimization.
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 | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".