Violence in healthcare at a glance: The example of the Turkish physician
Bibliographic record
Abstract
BACKGROUND: Violence is an important public health problem that threatens the peace of mind in health institutions as well as in many other sectors. In recent studies, it was stated that violence occurs more frequently in healthcare centers compared to other establishments. In terms of being exposed to violence, healthcare personnel is at 16 times more risk. Violence against healthcare employees has increased gradually in Turkey and the world; it's a serious occupational hazard to consider. AIMS: The aim of our study was to analyze the situation about violence against physicians, to emphasize its importance, to shed light on preventive measures and to create programs and to raise awareness of violence against physicians. METHODS: This descriptive study was conducted between October-December 2017. A link was sent to the participants to participate voluntarily through the social media platforms mostly used by physicians and they were asked to fill out a questionnaire. RESULTS: 83.3% of the 948 physicians that participated have experienced at least one episode of violence so far. The most common type of violence was verbal. More than half of violent events occurred in hospitals. One-third of the physicians did nothing in the face of violence, more than half could not report this situation due to lack of time, only one-quarter of those who sought support received it. Only 23.7% of the perpetrators were punished, while physicians saw the lack of deterrent sanctions as the main cause of violence. While 90.2% of physicians feel occupational burnout, 81.6% thought that physicians can protect themselves through effective legislation against violence in healthcare. CONCLUSION: The Law on Violence in Health should be enacted and put into practice as soon as possible.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".