S01.3 Using multiple data sources for programme evaluation: integration of program monitoring data with other research studies
Bibliographic record
Abstract
Background Integration of program monitoring data with focused research studies can be a powerful approach to program evaluation and outcome assessment. This paper draws on examples from a large HIV prevention program in Karnataka, India implemented by the University of Manitoba, and funded by the Bill & Melinda Gates Foundation. Methods Data sources included (1) routine program data to monitor coverage (2) semi-annual assessment of behavioural outcomes using rapid, unlinked anonymous methods called Polling Booth Surveys (PBS) (3) Integrated Behavioural and Biological Surveys (IBBS) and (4) mathematical modeling of HIV transmission dynamics. Results The program monitoring data indicated that the monthly coverage of the estimated female sex workers (FSWs) increased from 68% to 76% and the monthly clinical attendance increased from 19% to 27% over a one year period. PBS demonstrated that the condom use among FSWs in last sex with any client increased from 64% to 73% over four years. IBBS indicated that HIV prevalence among the FSWs declined from 25% at baseline to 13% at end line. The mathematical modeling which used parameters from these data sources suggested that a total of over 80,000 infections were averted by the Karnataka program. The monitoring and evaluation teams were embedded within the program, independently carrying out the design, data collection, analysis and feedback. Discussion The embeddedness of program monitoring and evaluation enabled regular feedback to program implementation in terms of which geographies to focus, which sub-groups to prioritize etc. Special intervention packages were implemented for the young and high-volume FSWs. Conclusion The examples presented here used interactive processes of data use throughout the program cycle through regular feedback to program implementation pon geographies/sub-populations that are lagging behind in terms of both coverage and quality. Disclosure No significant relationships. S01.4 Evaluating complex public health issue violence: understanding and measuring violence and evaluating violence interventions – lessons from STRIVE Sinead Delany-Moretlwe Wits Reproductive Health and HIV Institute, South Africa
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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 | 0.182 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.008 |
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".