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Record W4400093846 · doi:10.21203/rs.3.rs-4555351/v1

The Impact of COVID-19 Restrictions on the Health and Well-being of Women Living in Informal Settlements in Uganda.

2024· preprint· en· W4400093846 on OpenAlexafffund
Moses Tetui, Na-Mee Lee, Laseen Alhafi, Lesley Johnston, Susan Babirye, Warren Dodd, Chrispus Mayora, Shafiq Kawooya, Zeridah Nakasinde, Sharon I. Kirkpatrick, Zahid A Butt, Simon Kasasa, Mary Achom, Daniel Byamukama, Craig R. Janes

Bibliographic record

VenueResearch Square · 2024
Typepreprint
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversity of Waterloo
FundersUniversity of WaterlooInternational Development Research Centre
KeywordsCoronavirus disease 2019 (COVID-19)Well-beingHuman settlementInformal settlements2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)GeographyEnvironmental healthSocioeconomicsBusinessEconomic growthDemographic economicsMedicinePsychologyEconomicsVirologyDisease

Abstract

fetched live from OpenAlex

Abstract Background: The COVID-19 pandemic significantly impacted Uganda, with the first case reported in March 2020, resulting in extensive public health restrictions, including a lockdown, curfew, and closure of schools and workplaces. Urban residents, particularly those living in poverty in informal settlements, faced heightened challenges due to inadequate access to basic services, financial hardships, and increased caregiving responsibilities, especially for women. Women faced heightened risks of gender-based violence and engaged in transactional sex as coping mechanisms. This study explored the strategies used by women in Kampala and Mbale cities to meet basic needs during the pandemic and their implications for HIV infection vulnerability. Methods: Researchers conducted in-depth discussions with 282 women from various age groups in Kampala and Mbale's largest informal settlements, gathering insights into their pandemic experiences. These discussions, held in local languages, explored women's social, family, and financial challenges, as well as their perceptions of HIV risks. Transcripts were translated by local language experts before analysis. The team analyzed the transcripts using NVivo version 14 software, identifying patterns and themes that revealed survival strategies employed by women. Results: The study identified three interconnected themes that capture the complex strategies and challenges faced by women in informal settlements in Kampala and Mbale during the COVID-19 pandemic. Women struggled to cope with financial hardships and increased caregiving responsibilities, often resorting to desperate measures like transactional sex to survive, despite their resilience. The pandemic exacerbated vulnerabilities, heightening risks of HIV transmission and mental health issues, particularly among women living in poverty. While support networks provided some relief, they often fell short of meeting the diverse needs of women in these communities. Conclusions: The study shows that women in Uganda's informal settlements demonstrated resilience by taking on new roles and engaging in trading, but their reliance on transactional sex revealed stark power imbalances, increasing their vulnerability to gender-based violence, unintended pregnancies, and HIV infection. This highlights the urgent need for targeted interventions that address the complex challenges women face in crisis situations, which could enhance their resilience and alleviate their multiple struggles, with valuable lessons for similar contexts in low- and middle-income countries.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0020.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.046
GPT teacher head0.449
Teacher spread0.402 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

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