A Draconian Law: Examining the Navigation of Coalition Politics and Policy Reform by Health Provider Associations in Karnataka, India
Bibliographic record
Abstract
A comprehensive picture of provider coalitions in health policy making remains incomplete because of the lack of empirically driven insights from low- and middle-income countries. The authors examined the politics of provider coalitions in the health sector in Karnataka, India, by investigating policy processes between 2016 and 2018 for developing amendments to the Karnataka Private Medical Establishments Act. Through this case, they explore how provider associations function, coalesce, and compete and the implications of their actions on policy outcomes. They conducted in-depth interviews, document analysis, and nonparticipant observations of two conferences organized by associations. They found that provider associations played a major role in drafting the amendments and negotiating competing interests within and between doctors and hospital associations. Despite the fragmentation, the associations came together to reinterpret the intentions of the amendments as being against the interests of the profession, culminating in a statewide protest and strike. Despite this show of strength, provider associations only secured modest modifications. This case demonstrates the complex and unpredictable influence of provider associations in health policy processes in India. The authors' analysis highlights the importance of further empirical study on the influence of professional and trade associations across a range of health policy cases in low- and middle-income countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".