Learning from young people in Port Harcourt and Bayelsa, Nigeria, about their experiences of depression: a discussion space report
Bibliographic record
Abstract
Recently, we reported a morning-long discussion (held 7th February 2023) that we held with a group of youth currently living in Johannesburg, South Africa, and self-identifying as Black African (Levine et al., 2023). Black African young people in South Africa typically have the least access to mental health supports, given South Africa’s Apartheid history and ongoing racialised inequity. We wanted to understand their lived experiences and observations of the risks and influences that make African young people vulnerable to elevated levels of depression (i.e., strong feelings of hopelessness, despondency, and sadness). While young people can experience other mental health challenges, our narrow interest in depression was prompted by the knowledge that youth depression is a global emergency, particularly in under-resourced contexts such as Africa (Sankoh et al., 2018), and that African youth are typically under-represented in mental health studies (Steel et al., 2022). Given our long-standing and enduring attention to human resilience since the early 2010s (Theron, 2016; Theron et al., 2013; Theron & Ungar, 2023; Ungar, 2011, 2018, 2021; Ungar & Theron, 2020), we were also interested in learning what young people believed might support youth resilience to mitigate or counter these risks. Finally, we were curious about young people’s thoughts on the value of an empirical study that would produce insight into how best to protect young people living in Africa against elevated levels of depression.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".