Multisystemic resilience and its impact on youth mental health: reflections on co-designing a multi-disciplinary, participatory study
Bibliographic record
Abstract
Youth depression is a global emergency. Redressing this emergency requires a sophisticated understanding of the multisystemic risks and biopsychosocial, economic, and environmental resources associated with young people's experiences of no/limited versus severe depression. Too often, however, personal risks and a focus on individual-level protective resources dominate accounts of young people's trajectories towards depression. Further, studies of depression in high-income countries (i.e., "western") typically inform these accounts. This article corrects these oversights. It reports on the methodology of the Wellcome-funded R-NEET study: a multidisciplinary, multisystemic, mixed method longitudinal study of resilience among African youth whose status as "not in education, employment or training" (NEET) makes them disproportionately vulnerable to depression. Co-designed by academics, community-based service providers and youth in South Africa and Nigeria, with partnerships in the United Kingdom, Canada and the United States, the R-NEET study is identifying the physiological, psychological, social, economic, institutional, and environmental risks and resources associated with distinct trajectories of depression. Using the methodology of the R-NEET study as exemplar, this article advances an argument for understanding resilience as a contextually and culturally rooted capacity that draws on the multiple, co-occurring systems that young people depend upon to support their wellbeing. Acknowledging and harnessing the multiple systems implicated in resilience is critical to researchers and mental health providers who seek to support young people to thrive, and to young people themselves when protecting or promoting their mental wellbeing.
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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 | 0.231 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.039 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".