Adult Bullying in the Workplace and the Medical Field: A Narrative Review
Bibliographic record
Abstract
Studying and understanding adult bullying is integral to recognizing, acknowledging, and addressing attitudes and behaviours that may be deemed acceptable in a particular context but have debilitating consequences for individuals and organizations. Much of the research in this field has focused on the impact of bullying on the vulnerable populations: children and adolescents, and there is an overall scarcity in understanding the consequences of adult bullying. The literature supports the existence of characteristic differences between targets and perpetrators as the perpetrators lack self-awareness and empathy skills, while the targets are typically avoidant and submissive. In the workplace, perpetrators and targets relationship often involves a form of power imbalance, with perpetrators holding higher positions, and targets typically being low-status workers. Research has shown that up to 30 percent of American workers have been bullied in the workplace, including 11.3 percent healthcare workers. More than 75 percent of Canadian medical residents have reported being bullied, and 83 percent of medical students have experienced at least one incident of bullying or mistreatment. Such bullying incidents have a significant biopsychosocial burden on the victims and can negatively compromise one’s mental, physical, and social health, and create a pernicious work environment. Some of the conditions that have been associated with adult bullying include mood and anxiety disorders, suicidal ideation, heart and gastric diseases, and decreased job performance. In the medical field, all of such consequences could translate to a direct impact on patient care. Therefore, discussing and addressing adult bullying becomes a necessity.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".