Lingual Distribution of Tuberculosis Patients in Karachi - A Demographic Analysis
Bibliographic record
Abstract
The main objective of this paper is to investigate tuberculosis patients’ demographic distribution including their sociocultural impacts among various ethnic groups in the study area. Data was collected through questionnaire survey (interviewing patients) at TB diagnostic centers in the study area over a period of eight months (March to October 2013). Analysis was made with the help of Microsoft excel and SPSS version 20 for demographic analysis.The questionnaire survey revealed the respondents in terms of languages as Urdu (33.28), Sindhi (20.63), Punjabi (14.98), Pashtu (11.48), Seraiki (10.48%) and other languages 14.81. Researchers have identified other languages (which included, Hindko, Balti, Persian, Brahvi, Marwari, Gilgiti, Gujrati) as being the most vulnerable/impoverished lingual groups in the study area. Demographically, Urdu speaking TB patients were recorded as 53% females, but the case of Sindhi speaking female patients was slightly high (50.81%) compared to males. Punjabi speaking patients were found to number almost similar to the Urdu speaking patients as the percentage of females was 52.22%. Pashtu speaking females amounted to 44.9 %, while males recorded highest percentage i.e. 55.07%.The occurrence of TB in the purview of lingual distribution of population in Karachi provides an insight into the transmission of the disease especially in the context of the global as well as local environment, cultural and politico-economic scenario.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".