The prevalence and mental health correlates of exposure to offensive behaviours at work in Hungary: results of a national representative survey
Bibliographic record
Abstract
BACKGROUND: Within the last decades, a substantial number of reports have established bullying behaviours as a severe risk to the health and safety of workers. However, in Hungary, the severity of this issue remains largely unknown. Therefore, the current study aimed to 1) determine the prevalence of offensive workplace behaviours in the Hungarian working population and 2) examine the relationship between exposure to these offensive behaviours and certain mental health indicators. METHODS: The cross-sectional analyses of the present study are based on a sample of 13,104 active workers being representative of the Hungarian working population according to gender, age, educational level, and 18 occupational sectors. The mid-length version of the Copenhagen Psychosocial Questionnaire II (COPSOQ II) was used to measure workplace offensive behaviours (bullying, sexual harassment, threats of violence, and physical violence) in the 12 months preceding the survey. Examined mental health correlates included depressive symptomatology (Beck Depression Inventory), functional somatic symptoms (PHQ-15), perceived stress (Perceived Stress Scale), and general well-being (WHO Well-being Index). RESULTS: Almost half (48.7%) of the sample reported exposure to some form of offensive behaviour; 37.6% of participants reported occasional-, while 11.1% reported weekly or daily exposure. More women than men were exposed to offensive workplace behaviours, and those targeted the most were individuals aged 18-29 and in companies employing 20-49 employees. Top managers reported the lowest amount of bullying, while unskilled labourers reported the most frequent exposure. A moderately strong relationship was discovered between exposure to workplace offensive behaviours and all indicators of mental health. CONCLUSION: Workplace bullying was revealed to be a significant public health concern according to this large, representative data set from Hungary. Strategies to reduce the occurrence and impact of these behaviours on employee health should be a priority for occupational health and safety interventions.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".