The Socio-economic Impact of COVID-19 Among Women and Youth in Nakivale Refugee Settlement, Uganda
Bibliographic record
Abstract
Although COVID-19 prevention measures played an important role in containing the pandemic, they had unintended consequences especially among vulnerable populations. This formative research sought to explore the impact of COVID-19 on women and youth in the Nakivale Refugee Settlement (NRS), Uganda. In this mixed-methods study, we administered surveys to 258 women and youth aged 15–35 years from NRS to capture data including food insecurity, asset index, financial support and social support. We conducted four focus group discussions (FGDs) with 40 purposively selected participants composed of women, youth and leaders. Quantitative data were descriptively analysed using STATA 13. Qualitative data were analysed using thematic analysis. Participants reported severe food insecurity ( n = 242, 90%), probable depression ( n = 129, 50%), low social-economic status ( n = 136, 54%), loss of employment/income ( n = 204, 79%), saving ( n = 203, 78%), assets ( n = 202, 78%) and financial support ( n = 210, 81%). Results from FGDs indicate that COVID-19 resulted in significant losses in human, social, natural, physical and financial capital among refugee women and youth. Future emergency response strategies should be designed based on the differentiated needs of vulnerable women and youth focusing on ensuring continuous access to essential services such as inclusive and gender-sensitive access to health, education and livelihood services.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| 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".