Practitioner Review: Diagnosing childhood resilience – a systemic approach to the diagnosis of adaptation in adverse social and physical ecologies
Bibliographic record
Abstract
BACKGROUND: With growing interest in resilience among mental health care providers globally, there is a need for a simple way to consider the complex interactions that predict adaptive coping when there is exposure to high levels of adversity such as family violence, mental illness of a child or caregiver, natural disasters, social marginalization, or political conflict. METHODS: This article presents diagnostic criteria for assessing childhood resilience in a way that is sensitive to the systemic factors that influence a child's wellbeing. The most important characteristics of children who cope well under adversity and avoid problems like depression, PTSD, and delinquency are highlighted. RESULTS: A multidimensional assessment of resilience is presented that examines, first, the severity, chronicity, ecological level, children's attributions of causality, and cultural and contextual relevance of experiences of adversity. Second, promotive and protective factors related to resilience are assessed with sensitivity to the differential impact these have on outcomes depending on a child's level of exposure to adversity. These factors include individual qualities like temperament, personality, and cognitions, as well as contextual dimensions of positive functioning related to the available and accessibility of resources, their strategic use, positive reinforcement by a child's significant others, and the adaptive capacity of the environment itself. Third, an assessment of resilience includes temporal and cultural factors that increase or decrease the influence of protective factors. A decision tree for the diagnosis of resilience is presented, followed by a case study and diagnosis of a 15-year-old boy who required treatment for a number of mental health challenges. CONCLUSIONS: The diagnostic criteria for assessing resilience and its application to clinical practice demonstrate the potential usefulness of a systemic approach to understanding resilience among child populations.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".