Identification of Determinants of Resilience in Children Using Administrative Health, Social, Justice and Education Data
Bibliographic record
Abstract
IntroductionResilience is a key factor in healthy development of children who have experienced adversity in early life. Current methods of assessment involve using questionnaires - few of which are appropriate for young children, most are time consuming and rely on parent recall, thereby introducing bias. Additionally, widespread implementation of these would be costly, making population-level assessment of resilience impractical. The current study will leverage multi-sector, linkable, whole-population data from health, education, justice and social services to explore alternative ways to assess resilience in children. Objectives and ApproachThe purpose of this study is to identify factors in administrative data that emerge as significant determinants of resilience, demonstrated by children who experience adversity in early life but develop normally. Children born in Manitoba between 2000-2012 will be included. Adversity will be identified as families receiving income assistance, and/or the presence of adverse childhood experiences (ACEs) in linked databases such as Justice (incarcerated parent) and Health (parent with mental or substance use disorder). Development will be measured using the Early Development Instrument (EDI). EDI outcomes will be assessed according to severity and frequency of adversity. Pre-identified covariates that map onto sub-constructs of resilience will be assessed using multivariable linear regression to determine whether they are associated with higher EDI scores in the context of adversity. ResultsThe identification of administrative data variables associated with resilience in children will serve as a valuable tool for population-level assessment of resilience among children who experience adversity at young ages. Conclusion / ImplicationsWhile complete eradication of childhood adversity is unlikely, the continued development of programs and policies that build resilience in children is crucial. The results of this study will provide a means for population-level evaluation of such programs and policies, ultimately improving evidence for policy and decision-makers in the area of child development.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".