Patterns of individual coping, engagement with social supports and use of formal services among a five-country sample of resilient youth
Bibliographic record
Abstract
BACKGROUND: Although resilience among victims of child abuse is commonly understood as a process of interaction between individuals and their environments, there have been very few studies of how children's individual coping strategies, social supports and formal services combine to promote well-being. METHOD: For this study, we conducted a multi-phase analysis of a qualitative dataset of 608 interviews with young people from five countries using grounded theory strategies to build a substantive theory of young people's service and support use patterns. We started with an analysis of ten interviews (two from each country) and then compared these findings to patterns found in each country's full dataset. RESULTS: The substantive theory that emerged explains young people's transience between individual coping strategies (cognitive and behavioral), reliance on social supports (family members, peers and teachers), and engagement with formal service providers whose roles are to provide interventions and case management. Young people's patterns of navigation were shown to be contingent upon the individual's risk exposure, his or her individual capacity to cope, and the quality of the formal and informal supports and services that are available and accessible. CONCLUSION: Differing amounts of formal resources in low-, middle- and high-income countries influence patterns of service use. Implications for better coordination between formal mental health services and social supports are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".