Understanding disruption in the social contract between the medical profession and society in India: a tale of mismatched expectations?
Bibliographic record
Abstract
A harmonious relationship between the medical profession and the society it serves is essential for any country's health system to fulfill its mandate. Society offers trust, respect, authority, and professional autonomy to doctors, and in return, expects doctors to provide good care and prioritize people's welfare. However, in many parts of the world, we observe growing dissatisfaction, increasingly expressed violently, with the medical profession. Understanding what explains this growing dissatisfaction is necessary to initiate measures to maintain and improve this important social relationship and social contract. Using India as a case, and drawing on insights from qualitative, in-depth interviews with purposively selected doctors, journalists, legal experts, police, patients and patients' rights activists, and social commentators, we demonstrate how a range of mismatched expectations-regarding the organization of the medical profession, the structure of healthcare provision, the status and identity of doctors in society, and fair compensation for care provides-are contributing to the disruption of this critical social relationship. We argue that these dynamics can be meaningfully examined through the lens of the 'social contract' between the medical profession and the society it serves. Our analysis also shows how these mismatched expectations are highly contentious and how they are rooted in the increasingly market-logic-based organization of healthcare. For researchers across the world, our study offers a novel approach to researching the relationship between the medical profession and society, and, for policy makers and health system leaders in India, our findings offer practical entry points to develop policy interventions to help restore, recalibrate, and secure this important social contract.
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| 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".