The Effect of the Health Personnel Exposed to the Attack of Patients and Relatives on the Perception of Aggression
Bibliographic record
Abstract
Purpose: The aim of the study is to evaluate the effect of health personnel's exposure to the violence of patients and relatives on the perception of aggression. Materials and Methods: This cross-sectional study was conducted in 2015 among health personnel who are in contact with patients and their relatives working in health institutions in Yozgat city center. The study was completed with 358 people who agreed to participate in the study with verbal consent. The data were collected through the Perception of Aggression Scale (POAS), the socio-demographic form and a form that evaluates the health personnel being attacked. In the analysis of the data, univariate tests and multivariate regression analyzes were used. Results: Of the health personnel, 81.6% of them stated that they were exposed to the violence of the patients and their relatives during their professional career and 37.7% during the last 12 months. In the regression analysis, the perception of functional aggression was higher in those working in university hospitals, and lower in physicians (p <0.05). Dysfunctional aggression perception was lower in medical secretaries, family health center staff, and university hospital staff (p <0.05). No significant relationship was found between the perception of aggression and age, gender, education level, professional experience (years), and their exposure to attack during the past 12 months (p> 0.05). Conclusion: Health personnel are of the opinion that the aggressive behavior of the patients does not correspond to the situation they are in and there is no acceptable excuse for such behaviors.
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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 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".