Violencia en contra del personal de salud
Bibliographic record
Abstract
Violence against healthcare workers (HW) is a fundamental rights issue and a public health problem. Globally two-thirds of HW have experienced violence, half of them experiencing non-physical violence and nearly a quarter being exposed to physical violence. Physicians and nurses are at greater violence risk, and the most common locations where incidents occur are emergency rooms and psychiatric units.\nThis problem is multifactorial, and therefore, measures to prevent or mitigate violence must englobe factors and temporal phases of the event. Evidence shows that integrated guidelines with articulated feedback can effectively reduce violence. The 2018 MINSAL standard for prevention and mitigation of violence is a step in this direction. However, there is currently not public record of the implementation of this standard, nor is there a national public policy coordinating these efforts with a focus on prevention management and continuous feedback.\nMoving forward the perspective, in addition to quantifying the problem and identifying its causes in Chile, it is necessary to address the challenge of developing a national policy for the prevention of violence against HW. Such policy should aim to provide a comprehensive framework that incorporates feedback mechanisms, ensuring continuous improvement in the prevention and management of violence incidents.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".