Micro-planning at scale with key populations in Kenya: Optimising peer educator ratios for programme outreach and HIV/STI service utilisation
Bibliographic record
Abstract
Peer education with micro-planning has been integral to scaling up key population (KP) HIV/STI programmes in Kenya since 2013. Micro-planning reinforces community cohesion within peer networks and standardizes programme inputs, processes and targets for outreach, including peer educator (PE) workloads. We assessed programme performance for outreach-in relation to the mean number of KPs for which one PE is responsible (KP:PE ratio)-and effects on HIV/STI service utilisation. Quarterly programmatic monitoring data were analysed from October 2013 to September 2016 from implementing partners working with female sex workers (FSWs) and men who have sex with men (MSM) across the country. All implementing partners are expected to follow national guidelines and receive micro-planning training for PEs with support from a Technical Support Unit for KP programmes. We examined correlations between KP:PE ratios and regular outreach contacts, condom distribution, risk reduction counselling, STI screening, HIV testing and violence reporting by KPs. Kenya conducted population size estimates (PSEs) of KPs in 2012. From 2013 to 2016, KP programmes were scaled up to reach 85% of FSWs (PSE 133,675) and 90% of MSM (PSE 18,460). Overall, mean KP:PE ratios decreased from 147 to 91 for FSWs, and from 79 to 58 for MSM. Lower KP:PE ratios, up to 90:1 for FSW and 60:1 for MSM, were significantly associated with more regular outreach contacts (p<0.001), as well as more frequent risk reduction counselling (p<0.001), STI screening (p<0.001) and HIV testing (p<0.001). Condom distribution and reporting of violence by KPs did not differ significantly between the two groups over all time periods. Micro-planning with adequate KP:PE ratios is an effective approach to scaling up HIV prevention programmes among KPs, resulting in high levels of programme uptake and service utilisation.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".