Evaluating the sustainability of health programmes: A literature review
Bibliographic record
Abstract
Background: Evidence shows that fewer than 1% of all international development projects worldwide, including those in Nigeria, were evaluated at least 2 years after completion to learn what genuinely changed. With over 787 million US Dollars in official development assistance to Nigeria’s health sector in 2017, this seeming disinterest in assessing sustainability – particularly in light of the international commitments to the Sustainable Development Goals – is concerning. Objectives: We aim to assess the overall body of knowledge on the evaluation of sustainability of health programmes in Nigeria. Methods: We conducted a broad literature search, which included grey literature such as development project reports to identify all relevant studies reporting on our study objective. Articles were selected for inclusion using predefined criteria and data were extracted onto a purposely designed data extraction form. Results: Four articles met our search criteria. The review identified financial, technical, social and environmental barriers to sustainability. Recommendations encompassed all stages of the project cycle: funding, design, implementation, monitoring and evaluation. Conclusion: This review explored the overall body of knowledge on the evaluation of sustainability for health programmes in Nigeria. A clear understanding of operational indicators for sustainability, embedding sustainability early in the project cycle, community ownership, capacity building, effective collaboration, leadership and quality post evaluation are key for sustainable development in Nigeria. A limitation of this review is the small number of studies included and the assessment of sustainability at a single point in time. Much more empirical and rigorous research is needed to explore sustainability of health programmes in Nigeria. Research should also seek to understand the views of key stakeholders such as donors, implementing partners and the government.
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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 | 0.020 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".