Understanding the emergence of ‘Communitization’ under India’s National Rural Health Mission (NRHM): Findings from two Witness Seminars
Bibliographic record
Abstract
India's experience with the National Rural Health Mission (NRHM) is notable on account of nationally formalisingat scalecommunity action in service delivery, monitoring, and planning of health services.A study was undertaken to document and create a historical record of NRHM's 'communitization' processes.The oral history method of the Witness Seminar was adopted and two virtual seminars with five and nine participants, respectively, were conducted, and supplemented with 4 in depth interviews.Analysis of transcripts was done using ATLAS.ti22 with the broad themes of emergence, evolution, and evaluation and impact of 'communitization' under NRHM.This paper engages with the theme of 'emergence' and adopts the Multiple Streams Framework (MSF) conceptualised by John Kingdon for analysis.Key findings include the pioneering role of boundary spanning decision makers and the Jan Swasthya Abhiyan (JSA) in advocacy and design of 'communitization' structures, and the legacy of rights based social mobilizations and state-civil society partnerships in health during the 1990s influencing the ethos underlying 'communitization'.Democracy, leadership from the civil society in policy design and implementation, and state-civil society partnerships are linked to the positive results witnessed as part of 'communitization' in NRHM.
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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.028 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.002 | 0.010 |
| 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".