Learning from young people in Gauteng, South Africa, about their experiences of depression: a discussion space report
Bibliographic record
Abstract
Across the world, rising youth depression is a pressing concern that has animated researcher and popular media attention. Mostly, this attention has missed that youth depression can be prevented, provided we can discover and mobilize protective mechanisms at different systemic levels that have differential (i.e., the most impactful) value for specific youth in specific cultures and contexts. Importantly, we need to consider what combinations of protective mechanisms matter most and pay more attention to mechanisms in the systems that young people are connected to (like their families and communities). A handful of studies across a diversity of biological, social and environmental sciences are beginning to challenge the overly narrow focus on protective mechanisms at the level of the individual (Arango et al., 2021; Thapar et al., 2022). They show that a wide range of factors, like food security (Teasedale et al., 2021), less polluted or more temperate environments (Sugg et al., 2019; Theron et al., 2022), trusted social connections (Choi et al., 2020), opportunities for school and work engagement and social justice (Minh et al., 2021) matter as much, if not more, than individual resources for youth resilience to depression. Further, the Resilient Youth in Stressed Environments (RYSE) study is showing that these factors work in combination. RYSE-related work on the multisystemic determinants of the psychosocial resilience of youth living in stressed environments in Canada and South Africa (Ungar et al., 2021), has shown that at-risk youth report negligible depression symptoms when they also report greater access to a culturally relevant combination of relational, institutional and ecological resources (Theron et al., 2022). We wondered how these insights would hold up in stressed urban environments. We wanted to learn more about the intersection between multisystemic resources and young peoples’ real life experiences of mental poor health or depression in overcrowded, violent, stressed city spaces. To do this, we held a Discussion Space in Johannesburg in February 2023 to centre the voices of Black 18-24 year olds, and engage them in a participatory approach to navigate this emerging research landscape.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".