Flexible ngo-donor coordination in aid interventions to strengthen resilience: the case of Lebanon’s primary healthcare system
Bibliographic record
Abstract
INTRODUCTION: With shifts in international aid, international donors have increasingly regarded non-governmental organisations (NGOs) as capable of providing alternative public service arrangements. As funding flows to NGOs, particularly in contexts where both actors work towards strengthening health system resilience, NGO-donor relationships evolve. However, despite calls to investigate the contribution of relationships between actors within health systems, including NGOs and their donors, to health system resilience, empirical research is limited. Understanding these relationships is crucial for comprehending their role in fostering resilient health systems. This research fills this gap, by examining how NGO-donor coordination contributes to health system resilience in Lebanon. METHODS: This research focuses on Lebanon's primary health system, primarily managed by NGOs through contracts and heavily funded by donors. It examines NGOs operating under the national primary healthcare network (PHCN). The participants, including staff from these NGOs and donor agencies funding them, were purposively selected. 31 semi-structured interviews were conducted. The analysis framework relied on a thematic analysis. RESULTS: The findings revealed that the flexibility in NGO-donor coordination in Lebanon depends on donors' trust, regular coordination and donors' willingness to listen to NGOs' needs. In this light, they uncovered that flexible NGO-donor coordination enhances NGOs' resilience capacities in shocks, allowing them to operate flexibly. By strengthening NGOs' resilience, which contributes to the resilience of the broader health system, this relationship contributes to health system resilience. CONCLUSION: The findings contradict the mainstream development literature on NGO-donor relationships. The latter focuses on donor funding requirements that often result in rigid NGO-donor coordination, making it difficult for NGOs to be resilient. Rather, they emphasise the donors' role in implementing flexible development approaches, through flexible NGO-donor coordination, strengthening health system resilience. Overall, this paper contributes to the health system resilience literature by exploring how specific configurations of NGO-donor coordination strengthen health system resilience.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".