“My story is like a goat tied to a hook.” Views from a marginalised tribal group in Kerala (India) on the consequences of falling ill: a participatory poverty and health assessment
Bibliographic record
Abstract
BACKGROUND: Indigenous populations tend to have the poorest health outcomes worldwide and they have limited opportunities to present their own perspectives of their situation and shape priorities in research and policy. This study aims to explain low healthcare utilisation rates and opportunities to cope with illness among a deprived indigenous group - based on their own experiences and views. METHODS: A participatory poverty and health assessment (PPHA) was conducted among the Paniyas, a previously enslaved tribal population of South India in a Gram Panchayat in Kerala, India in 2008. Purposive sampling was used to select five Paniya colonies, involving 66 households. RESULTS: There were four key findings. First, Paniyas' perception that the quality of the public healthcare system is poor leads them to seek suboptimal care or deters them from using services. Second, there are significant costs of care unrelated to service use or purchase of medicines, such as travel costs, which the Paniyas lack the ability to pay. Third, illness can lead to loss of productive opportunities among those who fall ill and those who provide informal care. Fourth, the Paniyas lack a 'range' of coping strategies as they are wage labourers without diverse sources of income. They rely on a single strategy: borrowing from outside their community, often from landowners and employers, to whom they become indebted with their labour. CONCLUSIONS: Improving the capacity of tribal populations to present their own perspectives is likely to lead to more effective tribal development policies and consequently better health.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".