Frequency Distribution of Demographic Variables among Victims of Burns of Domestic Violence – A cross-sectional study
Bibliographic record
Abstract
Background: Burns are common occurrences of daily routine. The working environment and accidents invariably result in burns which unfortunately cannot be stopped. There definitely are certain factor which need to be assessed and addressed. Demographic variables like that of age, gender, marital status, family size, gross family income, literacy grades and occupation could be one of the vital aspects which need further research exploration to see frequencies of burns incidents. Aim: To assess the most involved demographic variables in frequency of burns incidents for domestic violence. Methods: The study population comprised of 250 unfortunate victim of burns of domestic violence reported in the Accident and Emergency Department of Mayo Hospital Lahore between December 2017 to August 2018. Results: The study revealed the minor age groups, females more than males, married adults, average family size, middle socio-economic class, lower grades of literacy and labor class subjects suffer the higher frequencies of burns incidents. Keywords: Age, Gender, Family Size, Marital Status, Income, Literacy Level, Occupation, Burns, Frequency
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".