A Qualitative Study Exploring Menstruation Experiences and Practices among Adolescent Girls Living in the Nakivale Refugee Settlement, Uganda
Bibliographic record
Abstract
(1) Background: Girls in low- and lower-middle income countries face challenges in menstrual health management (MHM), which impact their health and schooling. This might be exacerbated by refugee conditions. This study aimed at describing menstruation practices and experiences of adolescent girls in Nakivale refugee settlement in Southwestern Uganda. (2) Methods: We conducted a qualitative study from March to May 2018 and we intentionally selected participants to broadly represent different age groups and countries of origin. We conducted 28 semistructured interviews and two focus group discussions. Data were transcribed and translated into English. Analysis included data familiarization, manual coding, generation and refining of themes. (3) Results: Main findings included: (a) challenging social context with negative experiences during migration, family separation and scarcity of resources for livelihood within the settlement; (b) unfavorable menstruation experiences, including unpreparedness for menarche and lack of knowledge, limitations in activity and leisure, pain, school absenteeism and psychosocial effects; (c) menstrual practices, including use of unsuitable alternatives for MHM and poor health-seeking behavior. (4) Conclusions: A multipronged approach to MHM management is crucial, including comprehensive sexual education, enhancement of parent–adolescent communication, health sector partnership and support from NGOs to meet the tailored needs of adolescent girls.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".