From knowledge to action: participant stories of a population health intervention to reduce gender violence and HIV in three southern African countries
Bibliographic record
Abstract
This paper describes implementation research of an intervention in a complex HIV prevention randomised trial in southern Africa. Researchers collected stories of change attributed by 106 community members to an audio-drama edutainment intervention in 41 sites in Botswana, Namibia and Swaziland. The team analysed themes in the stories following a behaviour change model of conscious knowledge, attitudes, subjective norms, intention to change, agency, discussion and action (CASCADA). Storytellers attributed positive changes to the intervention in the areas of gender violence, multiple sexual partners, transactional and intergenerational sex and condom use. Their stories illustrate each of the steps in the CASCADA behaviour change model. As well as supporting an enabling environment for other interventions in the trial, the audio-drama also helped some participants to make personal changes. Collecting and discussing the stories were encouraging for the trial fieldworkers. Documenting the experiences of participants and framing the analysis of stories in an explicit behaviour change model allowed us to reflect on potential mechanisms and pathways through which the intervention impacts on individuals and communities. It helped in the design of the quantitative instruments to measure intermediate outcomes of the trial.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".