Applying photovoice and human-centered design to contextualize an adolescent micronutrient supplementation intervention in Mozambique
Bibliographic record
Abstract
BACKGROUND: Globally, iron-deficiency anemia is the most common micronutrient deficiency and a leading cause of disability-adjusted life years lost among adolescent girls 10-19 years of age. Adolescent girls' voices are often excluded from shaping the interventions and policies designed to support them. We used participatory formative research methods-photovoice and adolescent-centered design (ACD)-to explore nutrition-related challenges, opportunities, and preferences among adolescent girls in Monapo District, Mozambique, and used the findings to contextualize a nutrition curriculum and supplement delivery platform. METHODS: We purposively selected 16 girls from three rural and peri-rural secondary schools divided equally into two age groups (13-16 years and 17-20 years) and asked them to take photos of their food environment. Following a week of photo-taking, participants discussed their photos using the SHOWeD methodology in two workshops and in follow-up individual interviews. We also conducted three ACD group discussions with girls 13 to 20 years, each consisting of 10 to 12 participants, to explore consumption and supplement packaging preferences. RESULTS: Thematic analysis of photos and transcripts showed that participants preferred locally grown foods and indigenous protein sources and were actively engaged in agriculture and household chores, highlighting opportunities for nutritional improvement. However, their nutrition was constrained by seasonal food shortages, inequitable household responsibilities compared to boys, and limited social capital. While school-based supplementation is the standard practice, participants strongly preferred to take supplements at home to avoid stigma and benefit from the comfort and privacy of their own homes. We used these insights to refine the adolescent nutrition curriculum and design a multiple micronutrient supplementation delivery platform. CONCLUSIONS: Photovoice provided rich visual data about the lived experiences of adolescent girls in a fragile and resource-constrained context, without the influence of an external researcher interpreting everyday realities, and elicited valuable insights into the barriers, opportunities, and potential improvements in nutrition programming. Integrating photovoice and ACD into program design can increase program acceptability and potential for effectiveness. This research also highlights the need to prioritize adolescent engagement and underscores the inadequacy of one-size-fits-all approaches, such as school-based supplementation programs.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".