Health needs of older populations affected by humanitarian crises in low- and middle-income countries: a systematic review
Bibliographic record
Abstract
BACKGROUND: The combination of global demographic changes and a growing number of humanitarian crises in middle-income countries that have a higher life expectancy has led to an increase in the number of older populations affected by humanitarian crises. The aim of this review was to systematically examine evidence on the health needs of older populations in humanitarian crises, including both armed conflicts and natural disasters, in low- and middle-income countries (LMICs). METHODS: A systematic review methodology was used. The search strategy used terms related to older populations and humanitarian crises in LMICs. Five bibliographic databases were used, along with relevant grey literature sources. Descriptive analysis was used, and a quality assessment conducted using the Newcastle-Ottawa Scale and CASP instruments. RESULTS: A total of 36 studies were eligible for review. The majority of the studies were cross-sectional, three were cohort studies, and four used qualitative methodologies. The main health outcomes were mental health, physical health, functioning, and nutrition. Vulnerability factors included older age, female gender, being widowed, increased exposure to traumatic events, prior mental health problems, low income and education, and rural residency. Ten studies addressed the responsiveness of health systems and access to such services. The quality of the included studies was generally low. CONCLUSIONS: There is an urgent need to strengthen the evidence base on the health needs of older populations in humanitarian crises.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".