Violence against health care workers in rural areas of Sindh, Pakistan
Bibliographic record
Abstract
OBJECTIVE: To determine the magnitude of violence against healthcare workers in a rural setting, and the consequences of this violence on their personal and professional lives. METHODS: The descriptive, quantitative, cross-sectional study was conducted in 4 rural districts of the Sindh province of Pakistan from February to December 2019, and comprised healthcare workers, including doctors, nurses, support staff and field workers. Data was collected using a structured questionnaire. Data was analysed using SPSS 22. RESULTS: Of the 1622 subjects, 929(57.3%) were males and 693(42.7%) were females. The overall mean age was 35.55+/-10.05 years. The largest cluster was that of doctors 396(24.4%), followed by technicians 202(12.5%). Overall, 522(32.2%) subjects had a professional experience of 1-5 years. Violence at workplace in any form was experienced by 693(42.7%) subjects. Verbal violence had been experienced by 396(24.4%) subjects, while 228(14.1%) had witnessed it. The corresponding numbers for physical violence were 122(7.5%) and 22(1.4%). Verbal violence was more prevalent compared to physical violence (p<0.01). The major effect was that the healthcare workers remained alert 537(33.1%), felt frustrated 524(32.3%) and disturbed 503(31%). Also, 272(16.8%) subjects were planning to migrate or quit the profession. CONCLUSIONS: Violence was found to be a significant issue in rural Sindh.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".