657 – Nurses' Exposure to Workplace Bullying and Ptsd Symptomology: The Protective Role of Intrapersonal Resources
Bibliographic record
Abstract
Aim: The aim of this study was to examine the relationship between nurses’ exposure to workplace bullying and PTSD symptomology and the protective role of intrapersonal resources (psychological capital). Background: Workplace bullying has serious organizational and health effects in healthcare which threaten the quality of patient care. Few studies have examined the relation of workplace bullying to serious mental health outcomes, such as PTSD. In addition, the buffering effect of intrapersonal resources to protect nurses from effects of workplace bullying has not been studied. Method: A provincial survey of hospital nurses (n = 1205) was conducted to study the relationship between workplace bullying and PTSD and whether intrapersonal resources (Psycap) influenced this relationship. Nurses completed 3 standardized measures of bullying, PTSD, and Psycap. Results: A moderated regression analysis revealed that more frequent exposure to workplace bullying was significantly related to PTSD symptomology (R2 = .38). Psycap was not a significant moderator. Bullying exposure and Psycap were significant independent predictors of PTSD symptoms (β = .52 and -.21, respectively). Conclusions: Workplace bullying appears to have a positive relationship with PTSD, a serious mental health outcome. This effect was not mitigated by Psycap, posited to be a protective against workplace stressors. This suggests that workplace bullying is a serious threat to nurses’ health requiring attention of hospital management.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".