Mothers’ psychopathology and their adult offspring’s cortisol level in a Rwandan sample
Bibliographic record
Abstract
Background: Most studies on the influence of mothers' trauma-related psychopathology on their offspring's hypothalamic-pituitary-adrenal (HPA) axis functioning have been conducted in Western contexts. Furthermore, those studies have focused on the association between mothers' post-traumatic stress disorder (PTSD) and their offspring's HPA axis functioning. More research is needed among African populations exposed to mass violence to mitigate the intergenerational transmission of trauma. Aim: To investigate the link between mothers' PTSD and depression and their offspring's basal cortisol level. Setting: This cross-sectional study was conducted in two provinces of Rwanda (Kigali City and the Southern Province) among families of survivors of the 1994 genocide perpetrated against the Tutsi. Methods: A total of 45 dyads of mothers and their adult offspring were recruited. They answered questionnaires that measured sociodemographic characteristics, trauma exposure, PTSD and depression symptoms. Participants also provided saliva samples for cortisol extraction. Results: Mothers' depression was negatively associated with their offspring's overall basal cortisol level. There was no link between mothers' PTSD and their offspring's overall basal cortisol level. The relationship between the offspring's overall basal cortisol level and their own psychopathology was not significant. Conclusion: These preliminary findings showed an HPA axis disruption among offspring of mass violence-exposed and depressed mothers. Contribution: This study contributes to the literature by showing that depression is a relevant correlate of neuroendocrine functioning and should be investigated more consistently in research on the intergenerational consequences of trauma exposure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".