Where are the NGOs and Why? The Distribution of Health and Development NGOs in Bolivia
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The presence and influence of nongovernmental organizations (NGOs) in the landscape of global health and development have dramatically increased over the past several decades. The distribution of NGO activity and the ways in which contextual factors influence the distribution of NGO activity across geographies merit study. This paper explores the distribution of NGO activity, using Bolivia as a case study, and identifies local factors that are related to the distribution of NGO activity across municipalities in Bolivia. METHODS: The research question is addressed using a geographic information system (GIS) and multiple regression analyses of count data. We used count data of the total number of NGO projects across Bolivian municipalities to measure NGO activity both in general and in the health sector specifically and national census data for explanatory variables of interest. RESULTS: This study provides one of the first empirical analyses exploring factors related to the distribution of NGO activity at the national scale. Our analyses show that NGO activity in Bolivia, both in general and health-sector specific, is distributed unevenly across the country. Results indicate that NGO activity is related to population size, extent of urbanization, size of the indigenous population, and health system coverage. Results for NGO activity in general and health-sector specific NGO activity were similar. CONCLUSIONS: The uneven distribution of NGO activity may suggest a lack of co-ordination among NGOs working in Bolivia as well as a lack of co-ordination among NGO funders. Co-ordination of NGO activity is most needed in regions characterized by high NGO activity in order to avoid duplication of services and programmes and inefficient use of limited resources. Our findings also indicate that neither general nor health specific NGO activity is related to population need, when defined as population health status or education level or poverty levels. Considering these results we discuss broader implications for global health and development and make several recommendations relevant for development and health practice and research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it