The family doctor: health, kin testing and primary care in Patna, India
Bibliographic record
Abstract
Private primary care providers are usually the first site where afflictions come under institutional view. In the context of poverty, the relationship between illness and care is more complex than a simple division of responsibilities between various actors-with care given by kin, and diagnosis and treatment being the purview of providers. Since patients would often visit the provider with family members, providers are attuned to the patients' web of kinship. Providers would take patients' kinship arrangements into account when prescribing diagnostic tests and treatments. This paper terms this aspect of the clinical encounter as 'kin testing' to refer to situations/clinical encounters when providers take into consideration that care provided by kin was conditional. 'Kin testing' allowed providers to manage the episode of illness that had brought the patient to the clinic by relying on clinical judgment rather than confirmed laboratory tests. Furthermore, since complaints of poor health also were an idiom to communicate kin neglect, providers had to also discern how to negotiate diagnoses and treatments. Kinship determined whether the afflicted bodies brought to the clinics were diagnosed, whether medicines reached the body, and adherence maintained. The providers' actions make visible the difference that kinship made in how health is imagined in the clinic and in standardized protocols. Focusing on primary care clinics in Patna, India, we contribute to research that shows that kinship determines care and management of illnesses at home by showing that relatedness of patients gets folded in the clinic by providers as well.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".