Mental Health System Reform in Contexts of Humanitarian Emergencies: Toward a Theory of “Practice-Based Evidence”
Bibliographic record
Abstract
Humanitarian emergencies such as armed conflicts are increasingly perceived as opportunities to improve mental health systems in fragile states. Research has been conducted into what building blocks are required to reform mental health systems in states emerging from wars and into the barriers to reform. What is less well known is what work and activities are actually performed when mental health systems in war-affected resource-poor countries are reformed. Questions that remain unanswered are: What is it that international humanitarian aid workers and local experts do on the ground? What are the actual activities they perform in order to enable and sustain system reform? This article begins to answer these questions through ethnographic case studies of mental health system reform in Kosovo and Palestine. Based on the findings, a theory of "practice-based evidence" is developed. Practice-based evidence assumes that knowledge is derived from practice, rather than the other way around where practice is believed to be informed by systematic evidence. It is argued that a focus on practice rather than evidence can improving system reform processes as well as the provision of mental health care in a way that is sensitive to local contexts, structural realities, culture, and history.
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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 | 0.196 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.010 | 0.092 |
| Scholarly communication | 0.027 | 0.031 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".