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
Despite a growing body of literature, substantial variance remains between researchers, mental health experts, clinicians, and practitioners over the nature, structure, and contents of psychosocial interventions aimed at reducing the mental health burden in war-torn and postconflict societies. We conducted a focused and systematic review of the literature published over the last two decades on the most commonly used psychotherapeutic treatment modalities in medical and humanitarian interventions as represented by expert opinion, observational and qualitative or mixed-method studies, case reports, case control, and community-based studies, excluding randomized controlled trials (RCTs) and meta-analyses of RCTs. More specifically, we aimed at searching for best practices and supporting psychosocial interventions within the domain of adult mental health in civilian populations in low- and middle-income countries affected by protracted political violence, armed conflict, and wars. We noted the need to translate existing knowledge into action (know-do gap) and the critical importance of applying qualitative evidence-based knowledge that informs and supports collective interventions and best practices in medical and humanitarian assistance programs currently being undertaken.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 it