An Exploratory study on violence in a rural area near Bengaluru, Karnataka, India
Bibliographic record
Abstract
Background In India there has been an increased reporting of violence over the years and its magnitude varies from state-to-state. Most of the studies available in India are on violence occurring within the household like domesticintimate partner violence child abuse etc. There are very few studies available on violence that occurs outside the household in the community.ObjectivesObjectives of the study were to assess the burden of violence among people living in a rural area near Bengaluru to list out the different types causes and health consequences of violence to assess the opinion of the subjects on violence and to find out the various factors associated with violence.MethodsThis exploratory study was conducted in the villages coming under three Primary Health Centres in a rural area near Bengaluru over a period of3 months in the last quarter of 2016. The Study subjects were aged between 14 years to 60 years. The total sample size was 2013. A pretested structured questionnaire was applied anonymously as the survey tool to gather information.ResultsA total of 2046 subjects were surveyed of whom 159 7.7 experienced some kind of violence in the past one year. 54 were males 32.1 were high school educated 14.5 had received no formal education 89.9 were Hindus. The major form of violence was verbal abuse 83.6 followed by pushing around 12.6 slapping 10.7 hitting 9.4. 5.0 had abrasions and 3.0 had bleeding and pain.ConclusionsAbout 7 of the subjects had experienced violence. Verbal abuse was the most common type of violence. Substance use was significantly associated with violence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".