Using the HIV Prevention Self-Assessment Tools (PSAT) to assess and monitor sex workers HIV programmes in selected countries in Africa
Bibliographic record
Abstract
<ns3:p> <ns3:bold>Background:</ns3:bold> The HIV Prevention Self-Assessment Tools (PSATs) were developed by the Global Prevention Coalition as an easy-to-use tool for country-led review of national HIV prevention response against a global standardised set of programmatic components. As part of the South to South Learning Network (SSLN), country-level data on HIV prevention programmes for sex workers were collected by 10 African countries, using the PSAT to self-assess their HIV prevention progress. </ns3:p> <ns3:p> <ns3:bold>Methods:</ns3:bold> Data were collected August 2020 to July 2021 from participating countries. In each country, a technical team of 8-10 members completed the tool with support from the SSLN. The PSAT collects data for three programme domains: management, implementation and outcomes and sustainability, each of which comprises essential programme functions and elements. Once all elements are scored, the tool automatically calculates the final scores for each domain, on a scale of 1-5. Also, 15 interviews were conducted with 15 country leaders to understand the ease and usefulness of the PSAT process. </ns3:p> <ns3:p> <ns3:bold>Results:</ns3:bold> The overall PSAT scores ranged between 4.1 (Kenya) to 2.3 in Zambia. Of the three domains, Programme Management achieved the highest scores, with four countries (Kenya, Ghana, Zimbabwe, and South Africa) scoring more than four. High scores were seen in the Programme Implementation domain as well; five countries (Zimbabwe, South Africa, Mozambique, Malawi and Uganda) scored between 3 to 4. For Programme Outcomes and Sustainability, other than Kenya, which did not score the outcomes, all countries scored poorly with scores ranging between 3.8 to 1.5. Comparing PSAT scores with UNAIDS suggested epidemic metrics have shown that countries with the highest PSAT scores also have high condom use rates and significant reductions in HIV incidence from 2010 to 2019. </ns3:p> <ns3:p> <ns3:bold>Conclusions:</ns3:bold> This exercise has helped country’s leadership to self-reflect on their HIV prevention programmes, increase ownership and identify areas that need strengthening. </ns3:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".