Engaging youth in rural Uganda in articulating health priorities through Photovoice
Bibliographic record
Abstract
Youth living in rural Uganda represent over 20% of the country's population. Despite the size of this demographic segment of the population, there is a paucity of data on their health priorities. Engaging people in understanding their own health status has proven to be an effective mechanism for health promotion. The objective of this study was to use Photovoice, a community-based, participatory action research methodology, to understand the current health priorities of youth living in rural Uganda. Thirty-two students between the ages of 13 and 17 were recruited from four schools within the region of Soroti, Uganda. Participants were given a disposable camera and were asked to photograph situations that contributed or detracted from their health status. The cameras were then returned to the investigators and each photo taken by the participant was reviewed with the investigators during a semi-structured interview. Codes were applied to the photographs and organized into overarching themes. Each participant chose one to two photos that were most representative of their health priorities for a secondary analysis. Participants provided 499 photos that met the eligibility criteria. The most common themes presented in the photographs were 'hygiene' ( n = 73, 12.4%), 'nutrition' ( n = 69, 11.7%), and 'cleanliness' ( n = 48, 8%). 'Hygiene' ( n = 6, 14.6%) and 'exercise' ( n = 6, 14.6%) were the most common priorities articulated in the representative photographs. Photovoice proved to be an effective method to assess and express the health concerns of youth in rural Uganda. Study participants were able to articulate their health concerns and priorities through photographs and reflect on opportunities for health promotion through subsequent interviews.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".