Implementing sport and physical activity across each layer of the mental health and psychosocial support (MHPSS) pyramid for populations affected by displacement
Bibliographic record
Abstract
Physical activity (PA) and sport are increasingly recognized as integral parts of mental health and psychosocial support (MHPSS) programming within humanitarian response, for people affected by forced displacement. Nonetheless, the programming and implementation of physical activity within MHPSS responses remains inconsistent and largely ad hoc.. In this Short Communication, our team of multidisciplinary authors including academics, and practitioners from disciplines of psychiatry, psychology, physical therapy and sport for development, examine the implementation of sport and PA more broadly, across each layer of the Inter Agency Standing Committee (IASC) MHPSS Pyramid. We demonstrate how PA can be implemented at each layer, to improve MHPSS outcomes in humanitarian settings. We outline examples of how PA is being implemented across the IASC MHPSS pyramid, from ensuring access to inclusive and enabling environments (Layer 1), to strengthening participation in community-based PA and sport initiatives (Layer 2), delivering targeted programs with intentional mental health outcomes (Layer 3), and providing specialized physical activity promotion within clinical mental health services (Layer 4). This Short Communication provides guidance for humanitarian actors on integrating sport and PA across each layer of the IASC MHPSS pyramid.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".