Developing Digital Stories with Youth on Climate Change and HIV Vulnerabilities in Nairobi and Kisumu, Kenya: Methods and Reflections
Bibliographic record
Abstract
There is a growing focus on the linkages between climate change and HIV vulnerabilities in Eastern and Southern Africa through pathways such as sexual and gender-based violence and transactional sex. Kenya is a relevant context to understand these linkages, with one of the world’s largest HIV epidemics and increasing climate-related extreme weather events (EWE) such as drought and flooding. Yet, the lived experiences of Kenyan youth at the nexus of climate change and HIV vulnerabilities remains understudied. Digital storytelling is a promising research approach that can meaningfully engage marginalized populations in sharing their experiences and generating solutions for stigmatized topics such as HIV prevention. Digital storytelling involves the creation of short audio-visual clips that combine personal storytelling with images, voice-over narration, and sound effects. This approach can also be used as tool for education, advocacy, and youth-centred knowledge production. We developed and implemented a digital storytelling video workshop methodology with youth aged 16–24 ( n = 54) in two Kenyan regions: informal settlements in Nairobi and fishing communities in Kisumu. The two-day digital storytelling workshops focused on: (1) providing information on climate change and its pathways to sexual health outcomes, including HIV; (2) building youth capacity to share their lived experiences and generate solutions through storytelling; (3) teaching audiovisual production skills; and (4) promoting advocacy, awareness, and peer education on HIV and climate change-related issues. This article describes the development and implementation of this methodology. Youth reflections on their experiences participating in these digital storytelling workshops identified the following themes: (1) new insights on HIV and climate change; (2) developing new skills; (3) building community connections; and (4) feeling more empowered. Findings reveal how digital storytelling methods can amplify youth voices and in turn generate new insights on the intersection of climate change and HIV in low- and middle-income contexts such as Kenya.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".