Understanding the Mechanisms through Which Family Risk Affects Adolescent Mental Health: A Model of Multisystemic Resilience in Context
Bibliographic record
Abstract
There is substantial evidence that exposure to family adversity significantly and negatively impacts positive adolescent development by placing adolescents at increased risk of experiencing developmental difficulties, including conduct problems. Although the mechanisms responsible for these effects are still largely unknown, a novel line of inquiry in the resilience field conceptualizes positive adaptation, following exposure to atypical adversity, as resulting from complex interactions of systems at multiple ecological levels. The purpose of the present analysis was to apply this multisystemic resilience framework to the study of positive adaptation following exposure to family adversity in a sample of Canadian adolescents (n = 230; mean age 16.16, SD = 1.38) and South African adolescents (n = 421; mean age = 15.97, SD = 1.19) living in economically volatile communities dependent on the oil and gas industry. Cross-sectional survey data were used to investigate the mechanisms through which family adversity exercises its impact on adolescent conduct problems by accounting for their caregiving, peer, and community resources. Results of two moderated mediation analyses showed that family adversity impacts adolescent externalizing mental health negatively, via disrupted caregiving, when other resources are also considered. For the Canadian adolescents, these negative impacts were protectively moderated by peer support, but not moderated by appreciation for community traditions. In contrast, peer support showed no significant protective effect for the South African sample, while a strong appreciation for community traditions was positively and significantly associated with conduct difficulties. Contextual dynamics (e.g., social unrest) provide a plausible explanation for the discrepant results and bring attention to the importance of theorizing resilience in context.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".