Health system evaluation in conflict-affected countries: a scoping review of approaches and methods
Bibliographic record
Abstract
INTRODUCTION: Strengthening health systems in conflict-affected settings has become increasingly professionalised. However, evaluation remains challenging and often insufficiently documented in the literature. Many, particularly small-scale health system evaluations, are conducted by government bodies or non-governmental organisations (NGO) with limited capacity to publish their experiences. It is essential to identify the existing literature and main findings as a baseline for future efforts to evaluate the capacity and resilience of conflict-affected health systems. We thus aimed to synthesise the scope of methodological approaches and methods used in the peer-reviewed literature on health system evaluation in conflict-affected settings. METHODS: We conducted a scoping review using Arksey and O'Malley's method and synthesised findings using the WHO health system 'building blocks' framework. RESULTS: We included 58 eligible sources of 2,355 screened, which included examination of health systems or components in 26 conflict-affected countries, primarily South Sudan and Afghanistan (7 sources each), Democratic Republic of the Congo (6), and Palestine (5). Most sources (86%) were led by foreign academic institutes and international donors and focused on health services delivery (78%), with qualitative designs predominating (53%). Theoretical or conceptual grounding was extremely limited and study designs were not generally complex, as many sources (43%) were NGO project evaluations for international donors and relied on simple and lower-cost methods. Sources were also limited in terms of geography (e.g., limited coverage of the Americas region), by component (e.g., preferences for specific components such as service delivery), gendered (e.g., limited participation of women), and colonised (e.g., limited authorship and research leadership from affected countries). CONCLUSION: The evaluation literature in conflict-affected settings remains limited in scope and content, favouring simplified study designs and methods, and including those components and projects implemented or funded internationally. Many identified challenges and limitations (e.g., limited innovation/contextualisation, poor engagement with local actors, gender and language biases) could be mitigated with more rigorous and systematic evaluation approaches.
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 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 | 0.154 | 0.303 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.044 | 0.045 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".