MétaCan
Menu
Back to cohort
Record W6925214647 · doi:10.17605/osf.io/h46ga

The Moderating Role of Social Support on the Relationship Between Caregiver Adverse Childhood Experiences and Family Functioning

2022· other· en· W6925214647 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2022
Typeother
Languageen
FieldSocial Sciences
TopicQualitative research in health
Canadian institutionsnot available
Fundersnot available
KeywordsSocial supportFamily resilienceStressorModerationBiopsychosocial modelPsychological resiliencePsychological interventionMental healthMultilevel model

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0110.005
Scholarly communication0.0010.000
Open science0.0030.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.117
GPT teacher head0.450
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueOpen Science FrameworkSame topicQualitative research in healthFrench-language works237,207