Evaluation design for large-scale HIV prevention programmes: the case of Avahan, the India AIDS initiative
Bibliographic record
Abstract
BACKGROUND: Closing the HIV prevention gap to prevent HIV infections requires rapid, worldwide rollout of large-scale national programmes. Evaluating such programmes is challenging and complex, requiring clarity of evaluation purpose and evidential approaches substantively different to those employed for pilots and small programmes. OBJECTIVES: This paper describes the evaluation design for the implementation phase of Avahan, the India AIDS initiative, a large HIV prevention programme funded by the Bill and Melinda Gates Foundation. Avahan, which began in December 2003, has a 10-year charter to impact the Indian epidemic and its response by implementing an HIV prevention programme targeting core and bridge groups in 83 districts of six Indian states, transferring the programme to the Government of India, and disseminating programme learning. METHODS: The foundation commissioned an external process to design Avahan's evaluation framework. An independent advisory group oversees and guides course corrections in the execution of this framework. RESULTS: Avahan's evaluation framework comprises: trend and synthetic analysis of data from core, bridge and household biobehavioural surveys in a subset of intervention districts, denominator estimates and programme monitoring from all intervention districts, and government's antenatal surveillance (two sites per district in all districts); bespoke transmission dynamics modelling to estimate infections averted (subset of districts); cost effectiveness studies (subset of districts). In addition, there are other knowledge-building and quality-monitoring activities. CONCLUSION: Rather than a small set of monofocal outcome measures, scaled programmes require nuanced evaluations that approximate programmatic scale by collecting data with different levels of geographical scope, synthesize multiple data and methods to arrive at a composite picture, and can cope with continuous environmental and programme evolution.
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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 | 0.364 | 0.282 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".