Family adaptation in families of individuals with <scp>Down</scp> syndrome from 12 countries
Bibliographic record
Abstract
Our current understanding of adaptation in families of individuals with Down syndrome (DS) is based primarily on findings from studies focused on participants from a single country. Guided by the Resiliency Model of Family Stress, Adjustment, and Adaptation, the purpose of this cross-country investigation, which is part of a larger, mixed methods study, was twofold: (1) to compare family adaptation in 12 countries, and (2) to examine the relationships between family variables and family adaptation. The focus of this study is data collected in the 12 countries where at least 30 parents completed the survey. Descriptive statistics were generated, and mean family adaptation was modeled in terms of each predictor independently, controlling for an effect on covariates. A parsimonious composite model for mean family adaptation was adaptively generated. While there were cross-country differences, standardized family adaptation mean scores fell within the average range for all 12 countries. Key components of the guiding framework (i.e., family demands, family appraisal, family resources, and family problem-solving communication) were important predictors of family adaptation. More cross-country studies, as well as longitudinal studies, are needed to fully understand how culture and social determinants of health influence family adaptation in families of individuals with DS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".