Measuring resilience in children: a review of recent literature and recommendations for future research
Bibliographic record
Abstract
PURPOSE OF REVIEW: Understanding variability in developmental outcomes following exposure to early life adversity (ELA) has been an area of increasing interest in psychiatry, as resilient outcomes are just as prevalent as negative ones. However, resilient individuals are understudied in most cohorts and even when studied, resilience is typically defined as an absence of psychopathology. This review examines current approaches to resilience and proposes more comprehensive and objective ways of defining resilience. RECENT FINDINGS: Of the 36 studies reviewed, the most commonly used measure was the Strengths and Difficulties Questionnaire (n = 6), followed by the Child Behavior Checklist (n = 5), the Resilience Scale for Chinese Adolescents (n = 5), the Rosenberg Self-Esteem Scale (n = 4), and the Child and Youth Resilience Scale (n = 3). SUMMARY: This review reveals that studies tend to rely on self-report methods to capture resilience which poses some challenges. We propose a complementary measure of child resilience that relies on more proactive behavioral and observational indicators; some of our preliminary findings are presented. Additionally, concerns about the way ELA is characterized as well as the influence of genetics on resilient outcomes prompts further considerations about how to proceed with resiliency research.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".