Understanding Early Childhood Resilience Following Neonatal Brain Injury From Parents’ Perspectives Using a Mixed-Method Design
Bibliographic record
Abstract
OBJECTIVES: The current study used a mixed-method design to qualitatively examine parents' definitions of resilience and factors they believed optimized their child's early outcome following neonatal brain injury. This was followed by quantitative analyses of early developmental and mental health outcomes and their relation to salient biopsychosocial factors. METHODS: Participants were parents of children diagnosed with neonatal brain injury due to stroke or hypoxic-ischemic encephalopathy (N=51; age range of children 18 months to 8 years). The Parent Experiences Questionnaire (PEQ) was used to qualitatively analyze parents' open-ended responses about their child's early experiences and outcome. The Child Behavior Checklist (CBCL) and Scales of Independent Behaviour Early Developmental Form (SIB-ED) parent ratings were used to measure child resilience from a quantitative perspective, identifying "at-risk" and "resilient" children using standard cutoffs. "Resilient" and "at-risk" children were compared on biopsychosocial variables using univariate t tests and chi-square analyses. RESULTS: Parents provided five unique definitions of their child's positive outcomes, and many children demonstrated resilience based on parent perspectives and quantitative definitions. Supporting factors included close medical follow-up, early intervention, and intrinsic factors within the child and parent. Group comparisons of "resilient" and "at-risk" children highlighted the importance of parent mental health across these early developmental and mental health outcomes. CONCLUSIONS: Many children were described as resilient during the early years by parents using qualitative and quantitative approaches. Findings highlighted the importance of parent well-being in promoting optimal early outcomes. (JINS, 2019, 25, 390-402.).
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".