Livelihood challenges of single female household heads in the Rohingya and host communities in Cox’s Bazar, Bangladesh during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Following the mass influx of Rohingya refugees into Cox's Bazaar, Bangladesh in 2017, makeshift settlement camps in Ukhiya and Teknaf have been overburdened, leading to livelihood challenges for both Rohingya and host communities. The humanitarian crisis has had adverse effects on vulnerable populations, which include older people, persons with disabilities, adolescents, and single female household heads. Using a subset of a larger dataset on households with most vulnerable groups in both communities, we analysed the effect of the pandemic and lockdown on the livelihood of single female household (HH) heads. METHODS: A cross-sectional household roster survey was designed to collect data from households with most vulnerable groups (MVGs) of host and Rohingya communities from December 2020 to March 2021; 11 host community villages and 10 Rohingya camps purposively selected as per the affiliated intervention of the project. The paper analysed quantitative and qualitative data from the sub-group of single female household heads without any income/low income. Participants were surveyed for their socio-demographic characteristics, COVID-19 experiences and knowledge, food security situation, social experiences and mental health using PHQ-2 test for depression. RESULTS: We surveyed 432 single female HH heads. Support during the pandemic was reported to be low, with less than 50% of HHs reporting relief meeting their needs; only 36% and 15% of these HHs received rations in camps and host communities respectively. Loan facilities were mostly unavailable and there were reported insufficiencies in food consumption. Over 50% of respondents tested positive on the PHQ-2, a scale used to screen for depression. Further analyses indicates that having a chronic health issue (OR 2.2, 95% CI 1.33-3.66) was positively associated with the PHQ-2 score for Rohingya single females. For host single females, having an ill member in the HH (OR 1.46, 95% CI 1.02-2.08) and the inability to save before the pandemic (OR 1.57 95% CI 1.11-2.23) increased the odds of screening positive for depression. CONCLUSION: Our study findings revealed insufficiencies with economic opportunities and food security for single female-headed households, as well as a high rate of positive screening for depression amongst this population. These findings call for a more in-depth understanding of the needs of this group.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".