The impacts of COVID-19 on older adults in Uganda and Ethiopia: Perspectives from non-governmental organization staff and volunteers
Bibliographic record
Abstract
The COVID-19 pandemic had a substantial impact on older adults, especially in Sub-Saharan Africa (SSA). To support older adults during this time, non-governmental organizations (NGOs) coordinated programs to help provide for basic needs related to food and water security and healthcare. This research explores the attitudes, perceptions and experiences of NGO staff and volunteers who provided support to older adults in SSA in rural East Africa during the COVID-19 pandemic. In-depth interviews (n = 28) were conducted with NGO staff and volunteers in Uganda and Ethiopia between September and December of 2022. Overall, NGO staff and volunteers reported high levels of knowledge surrounding the COVID-19 pandemic and stated that one positive of the COVID-19 pandemic was the improved hygiene practices. However, the NGO staff and volunteers also reported that the pandemic and the associated public health measures exacerbated pre-existing social inequalities, such as increasing pre-existing levels of food insecurity. The exacerbation of pre-existing social inequalities may be one reason for the increased reliance on NGO services. The learnings from the COVID-19 pandemic and associated public health measures can be utilized to create targeted strategies to mitigate the negative impacts of future public health crises on vulnerable populations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".