Workplace violence and the meaning of work in healthcare workers: A phenomenological study
Bibliographic record
Abstract
BACKGROUND: Workplace violence (WPV) has been associated with turnover intentions and reduced job satisfaction, yet the mechanisms behind such associations are still nebulous. Studying the way people make sense of their work in the context of WPV could lead to a better understanding of its consequences. PURPOSE: The objective of this exploratory study is to identify key features of meaning of work (MOW) in a group of healthcare workers and explain how these features can change following an act of WPV. METHODS: Researchers recruited 15 healthcare workers (11 women - 4 men) who had previously been the victim of a serious physical or sexual assault by a patient. A phenomenological approach was used. RESULTS: Two main themes were identified: MOW and relationships with others and MOW and relationship with the self. WPV might have the potential to trigger negative changes in the way some workers perceive their colleagues, their patients and their organisation. It can also interfere with their sense of self-accomplishment; all workers however, were still able to find positive meaning in 'contribution' and 'autonomy'. CONCLUSION: WPV has the potential to change certain aspects of MOW that could help explain why WPV is associated with lowered job satisfaction, compassion fatigue, and higher turnover. Also, finding meaning through contribution and autonomy can be a form of resilience.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".