Incarcerated population in India: how many are dying? How are they dying?
Bibliographic record
Abstract
Purpose This study aims to examine sociodemographic characteristics, levels and patterns of mortality experiences amongst Indian prisoners over the past two decades (1998–2018). Design/methodology/approach This study used prison statistics in India to analyze occupancy rate, percentage distribution, annual/decadal change, male–to–female ratios, prison mortality rate and causes of natural/unnatural deaths. Findings During 1998–2018, prisons in India grew by 18% and prisoners by 69%, leading to overcrowded jails. Males outnumbered female prisoners. Seventy percent of prisoners had an educational attainment level lower than 10th grade. In 2018, over 14 per 1,000 prisoners suffered from a mental illness and 384 per 100,000 died. Unnatural deaths accounted for 8%–11% of all prisoner deaths; 84% were by suicide. Illness accounted for 95% of all natural deaths in 2018; one–quarter was due to heart diseases. Research limitations/implications The study did not establish an association between sociodemographic characteristics with mental illness and mortality due to the non-availability of data. Social implications The pattern of a deteriorating living environment, rise in mental illnesses and mortality among Indian prisoners calls for immediate action from the authorities to protect them. Almost all unnatural deaths were by suicide (mostly by hanging). This detailed study would help authorities to take corrective measures for prisoner safety and well-being. There is also a need to develop a scientific database for this population. Originality/value To the best of the authors’ knowledge, this is the first study to examine morbidity and mortality experiences of the prisoner population using national statistics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".