A qualitative pilot study of food insecurity among Maasai women in Tanzania
Bibliographic record
Abstract
INTRODUCTION: Food insecurity is an ongoing threat in rural sub-Saharan Africa and is complicated by cultural practices, the rise of chronic conditions such as HIV and land use availability. In order to develop a successful food security intervention program, it is important to be informed of the realities and needs of the target population. The purpose of this study was to pilot a qualitative method to understand food insecurity based on the lived experience of women of the Maasai population in the Ngorongoro Conservation Area of Tanzania. METHODS: Short semi-structured qualitative interviews with 4 Maasai women. RESULTS: Food insecurity was present in the Maasai community: the participants revealed that they did not always have access to safe and nutritious food that met the needs of themselves and their families. Themes that emerged from the data fell into three categories: Current practices (food sources, planning for enough, food preparation, and food preservation), food Insecurity (lack of food, emotions, coping strategies, and possible solutions), and division (co-wives, food distribution, and community relationships). CONCLUSION: This pilot study suggested the presence of food insecurity in the Maasai community. Larger sample studies are needed to clarify the extent and severity of food insecurity among this population. Having a detailed understanding of the various aspects of the food insecurity lived experience could inform a targeted intervention program.
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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 | 0.005 | 0.005 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".