The contributions of religious leaders in addressing food insecurity during the COVID-19 pandemic in the Philippines: A realist evaluation of the Rapid Emergencies and Disasters Intervention (REDI)
Bibliographic record
Abstract
To address the unintended consequences of public health measures during the COVID-19 pandemic (e.g., emergency food insecurity, income loss), non-governmental organizations (NGOs) have partnered with diverse actors, including religious leaders, to provide humanitarian relief in resource-constrained communities. One such example is the Rapid Emergencies and Disasters Intervention (REDI), which is an NGO-led program in the Philippines that leverages a network of volunteer religious leaders to identify and address emergency food insecurity among households experiencing poverty. Guided by a realist evaluation approach, the objectives of this study were to identify the facilitators and barriers to effective implementation of REDI by religious leaders during the COVID-19 pandemic and to explore the context and mechanisms that influenced REDI implementation. In total, we conducted 25 virtual semi-structured interviews with religious leaders actively engaged in REDI implementation across 17 communities in Negros Occidental, Philippines. Interviews were audio recorded, transcribed, and thematically analyzed. Three main context-mechanism configurations were identified in shaping effective food aid distribution by religious leaders, including program infrastructure (e.g., technical and relational support from partner NGO), social infrastructure (e.g., social networks), and community infrastructure (e.g., community assets as well as a broader enabling environment). Overall, this study contributes insight into how the unique positionality of religious leaders in combination with organizational structures and guidance from a partner NGO shapes the implementation of a disaster response initiative across resource-constrained communities. Further, this study describes how intersectoral collaboration (involving religious leaders, NGOs, and local governments) can be facilitated through an NGO-led disaster response network.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".