The Moderating Role of Social Support on the Relationship Between Caregiver Adverse Childhood Experiences and Family Functioning
Bibliographic record
Abstract
Adverse Childhood Experiences (ACEs) can be passed onto future generations through complex biopsychosocial mechanisms. However, the presence of social support can lead to resilience and adaptation in caregivers who have experienced early adversity, reducing these negative intergenerational outcomes. Most research on the intergenerational consequences of ACEs has focused on mental health in subsequent generations, while overlooking family functioning as an outcome variable. Thus, the present study addresses this gap by examining the association linking caregiver ACEs (before the age of 18) and current family functioning, and the moderating role of current social support, while controlling for the proximal stress of COVID-19. Data will come from two samples of families: a multinational non-clinical sample (n=549), and a sample of families referred to a clinic in Toronto, Ontario (n=143). In both samples, self-report measures were completed by caregivers to assess caregiver ACEs, family functioning, social support, and family stressors due to COVID-19. Multiple regression analysis will be conducted to test cross-sectional moderation hypotheses. Results of this study will inform recommendations to potentially reduce the intergenerational transmission of ACEs using family-focused interventions and policies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".