An Implementation Research Approach to Evaluating Health Insurance Programs: Insights from India
Bibliographic record
Abstract
One of the distinguishing features of implementation research is the importance given to involve implementers in all aspects of research, and as users of research. We report on a recent implementation research effort in India, in which researchers worked together with program implementers from one of the longest serving government funded insurance schemes in India, the Rajiv Aarogyasri Scheme (RAS) in the state of undivided Andhra Pradesh, that covers around 70 million people. This paper aims to both inform on the process of the collaborative research, as well as, how the nature of questions that emerged out of the collaborative exercise differed in scope from those typically asked of insurance program evaluations. Starting in 2012, and over the course of a year, staff from the Aarogyasri Health Care Trust (AHCT), and researchers held a series of meetings to identify research questions that could serve as a guide for an evaluation of the RAS. The research questions were derived from the application of a Logical Framework Approach ("log frame") to the RAS. The types of questions that emerged from this collaborative effort were compared with those seen in the published literature on evaluations of insurance programs in low- and middle-income countries (LMICs). In the published literature, 60% of the questions pertained to output/outcome of the program and the remaining 40%, relate to processes and inputs. In contrast, questions generated from the RAS participatory research process between implementers and researchers had a remarkably different distribution - 81% of questions looked at program input/processes, and 19% on outputs and outcomes. An implementation research approach can lead to a substantively different emphasis of research questions. While there are several challenges in collaborative research between implementers and researchers, an implementation research approach can lead to incorporating tacit knowledge of program implementers into the research process, research questions that are more relevant to the research needs of policy-makers, and greater knowledge translation of the research findings.
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 | 0.050 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| 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".