Use of population measures and norms to identify resilient outcomes in young people in care: an exploratory study
Bibliographic record
Abstract
ABSTRACT The purposes of this study were to derive a new method for identifying resilience (i.e. positive adaptation in spite of serious adversity) among young people in care and to determine the percentage of the latter who experienced resilience on selected outcomes, as conceptualized from within the developmental approach of Looking After Children. The participants comprised two samples of young people who were living in out‐of‐home care (mainly foster care) in the province of Ontario, Canada, 340 aged 10–15 years and 132 aged 5–9 years. Virtually all had experienced severe adversity in their families of origin, such that in most cases the legal custody, care, and control of the young people had been permanently transferred from their parents to a local Children's Aid Society. Corresponding to each in‐care sample was a general‐population sample of the same age range that served as a normative comparison group and was drawn from the National Longitudinal Survey of Children and Youth (NLSCY). The NLSCY is an ongoing, long‐term social‐policy study of the development of a nationally representative sample of Canadian children into adolescence and early adulthood. The general‐population samples were composed, respectively, of 5539 young people aged 10–15 years and 11 858 children aged 5–9 years. Resilience among the young people in care was operationally defined, on each outcome variable, as average or above‐average functioning relative to that of the general‐population sample of the same age range. The percentage experiencing resilience was relatively high on the outcomes of health, self‐esteem, and pro‐social behaviour, moderate on the outcomes of relationship with friends and anxiety and emotional distress, and low on the outcome of academic performance. The implications of the findings 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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".