Addressing culture and context in humanitarian response: preparing desk reviews to inform mental health and psychosocial support
Bibliographic record
Abstract
Delivery of effective mental health and psychosocial support programs requires knowledge of existing health systems and socio-cultural context. To respond rapidly to humanitarian emergencies, international organizations often seek to design programs according to international guidelines and mobilize external human resources to manage and deliver programs. Familiarizing international humanitarian practitioners with local culture and contextualizing programs is essential to minimize risk of harm, maximize benefit, and optimize efficient use of resources. Timely literature reviews on traditional health practices, cultural beliefs and attitudes toward mental health and illness, local health care systems and previous experiences with humanitarian interventions can provide international practitioners with crucial background information to improve their capacity to work efficiently and with maximum benefit. In this paper, we draw on experience implementing desk review guidance from the World Health Organization (WHO) and UNHCR, the United Nations Refugee Agency (2012) in four diverse humanitarian crises (earthquakes in Haiti and Nepal; forced displacement among Syrians and Congolese). We discuss critical parameters for the design and implementation of desk reviews, and discuss current challenges and future directions to improve mental health care and psychosocial support in humanitarian emergencies.
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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 | 0.136 | 0.385 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".