The process of developing an augmented reality (AR) tool for knowledge translation on climate change-related experiences among youth in Kenya
Bibliographic record
Abstract
This report details a qualitative methodological approach of developing an Augmented Reality (AR) tool which integrates digital storytelling for context-specific, accessible, scalable participatory research knowledge translation on climate-related sexual health experiences among youth (aged 16-25 years) in Kenya. AR, which engages audiences through virtual images overlayed on the real world in real-time, enhances learning and knowledge retention. This suggests the potential for using this increasingly accessible technology in knowledge translation, despite such use being understudied. Our AR tool meaningfully incorporates seven digital storytelling videos made by youth in Kenya through a study in 2023, to amplify youth voices while illustrating complex pathways between four climate-related factors (drought, floods, extreme heat, and excess winds) and three HIV vulnerabilities (gender-based violence, early marriage, and transactional sex). The aim of this paper is to describe the design of an AR tool for knowledge translation, youth empowerment, and health promotion, and to outline how it can be harnessed for sexual health and climate change education to inform future knowledge translation approaches with youth climate-affected issues.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".