Resilience to depression: the role of benevolent childhood experiences in a South African sample
Bibliographic record
Abstract
Background: Studies elsewhere show that benevolent childhood experiences (BCEs) have protective mental health value. However, this protective value has never been investigated in an African context. Given the need to better understand what might support mental health resilience among African young people, this study explores the relationship between BCEs and depressive symptoms among a South African sample of young adults living in a community dependent on the economically volatile oil and gas industry. Methods: = 313, mean age 20.3 years, SD = 1.83, range from 18 to 26; majority Black African) completed self-report questionnaires to assess BCEs and depressive symptoms (Beck Depression Inventory-II). The analysis controlled for socio-demographics and experience of family adversity. Multinomial logistic regressions were used to examine the association of BCEs with depressive symptoms using STATA 17. Results: The majority (86.4% of the sample) reported all 10 BCEs. Of the 10 BCEs, having at least one good friend was the most reported (94%) compared to 75% of the sample reporting having a predictable home routine, such as regular meals and a regular bedtime. The unadjusted multinomial logistic regression analysis indicated that having at least one good friend, comforting beliefs, and being comfortable with self were associated with lower odds of moderate depression. The adjusted results showed no association between BCEs and the depression of young adults in this sample. Conclusion: In this South African sample, our results do not show protective associations between BCEs and depression. This could be as a result of the homogeneity in our sample. It is also possible that the BCEs explored could not counteract the effect of chronic risk factors in the lives of the young people in this study context. Further research is needed to understand this complexity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".