Addressing culture and context in humanitarian response: preparing desk reviews to inform mental health and psychosocial support
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it