Occupational violence toward mental health professionals during the COVID-19 pandemic: an international study
Bibliographic record
Abstract
Introduction: According to pre-COVID-19 pandemic studies, occupational violence (OV) toward mental healthcare professionals (MHCPs) is a common phenomenon with important consequences for their own mental health. This study sought to assess the prevalence of different types and sources of OV toward MHCPs during the COVID-19 pandemic, analyze the risk for OV conferred by relevant factors, and compare the emotional distress reported by MHCPs with and without OV. Methods: The study is an international cross-sectional Internet-based study completed by 3,325 MHCPs having provided direct clinical services during the COVID-19 pandemic. Results: 13.11% experienced OV. The most frequent type/source of OV was psychological violence inside the workplace (59.6% of those who reported OV). Risk factors for any type/source of OV being younger, working in emergency services, treating COVID-19 patients, and living in a lower to upper middle-income country. Emotional distress was higher in those who had experienced OV. Risk factors for emotional distress among those reporting OV included being younger and having experienced physical violence outside the workplace. Discussion: Approximately one in ten MHCP experienced OV during the COVID-19 pandemic. This figure is consistent with the range of OV against MHCPs reported prior to the pandemic and indicates that efforts are needed to prevent and manage OV and its negative emotional consequences among MHCP, particularly in aforementioned high-risk groups during health emergencies, and addressing both proximal and distal environmental factors related to OV toward MHCPs.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".