<i>‘We’re More Prepared than Before</i> : Understanding the Strategies Used by a Non-governmental Organization During the COVID-19 Pandemic in Sub-Saharan Africa
Bibliographic record
Abstract
IntroductionThe COVID-19 pandemic had a negative impact on populations worldwide, particularly on older adults residing in low - and middle-income countries. Due to these negative impacts, non-governmental organizations (NGOs) provided extensive support, which affected their operations.MethodsUsing the social resilience framework, the purpose of this study was to better understand what strategies NGOs used to support vulnerable populations and how they are building back stronger from the COVID-19 pandemic. In the fall of 2022, 26 (virtual) in-depth interviews were conducted with staff and volunteers from an NGO supporting older adults in Uganda.ResultsSeveral key themes emerged including using existing resources to better support older adults and staff and the importance of having multiple sources of revenue to support organizational operations.DiscussionThe key lessons learned by NGO staff and volunteers can be utilized to enact policy and practice change to help strengthen NGOs' social resilience. This would allow them to continue implementing innovative strategies to support vulnerable populations during times of crisis.
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 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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".