Lessons from humanitarian clusters to strengthen health system responses to mass displacement in low and middle-income countries: A scoping review
Bibliographic record
Abstract
The humanitarian cluster approach was established in 2005 but clarity on how lessons from humanitarian clusters can inform and strengthen health system responses to mass displacement in low and middle-income countries (LMIC) is lacking. We conducted a scoping review to examine the extent and nature of existing research and identify relevant lessons. We used Arksey and O'Malley's scoping framework with Levac's 2010 revisions and Khalil's 2016 refinements, focussing on identifying lessons from discrete humanitarian clusters that could strengthen health system responses to mass population displacement. We summarised thematically by cluster. Of 186 sources included, 56% were peer-reviewed research articles. Most related to health (37%), protection (18%), or nutrition (13%) clusters. Key lessons for health system responses included the necessity of empowering women; ensuring communities are engaged in decision-making processes (e.g. planning and construction of camps and housing) to strengthen trust and bonds between and within communities; and involving potential end-users in technological innovations development (e.g. geographical information systems) to ensure relevance and applicability. Our review provided evidence that non-health clusters can contribute to improving health outcomes using focussed interventions for implementation by government or humanitarian partners to inform LMIC health system responses to mass displacement.
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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 | 0.017 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".