Bibliographic record
Abstract
To investigate the ratio and characteristics of aggression, threat and physical violence directed towards staff in emergency departments as a model of state hospitals. A questionnaire were filled in by the staff working in the emergency department of three high-volume inner-city state hospitals. The individualized data collected were relevant to the pattern of violence, age, sex, number of years in the profession, nature of the job, and the behavioral characteristics of assailants, and outcome of incidents. The data were abstracted between 1 May and 31 May 2006. A total of 109 staff reports were reviewed. The relationship of aggression with sex, age and years of experience were insignificant ( P values were 0.464, 0.692, and 0.298, respectively), while profession was very significantly related ( P = 0.000). The relation between threat and sex is P = 0.311, experience 0.994, profession 0.326, age 0.278. The relationship of threat with sex, years of experience, profession and age were insignificant ( P values were 0.311, 0.994, 0.326, and 0.278, respectively). On the other hand, physical assault was found significantly related to sex, years of experience, profession and age ( P values were 0.042, 0.011, 0.000, and 0.000, respectively). Violence to the staff is common. There is not a significant relationship between aggression, threat and personal characters. However, male sex, >5 years experience, emergency doctor, ≥31 years of age are the risk factors for physical violence.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".