The associations between resilience and socio-demographic factors in parents who care for their children with congenital heart disease
Bibliographic record
Abstract
Objective: To examine the resilience of parents of children with congenital heart disease and to investigate socio-demographic factors that may influence parents' resilience. Methods: This is a web-based survey study using a cross-sectional design. A purposive sampling method was utilized to recruit 515 parents who care for children with congenital heart disease. Resilience was assessed using the Dispositional Resilience Scale-Ⅱ. Based on expert-interviews, a questionnaire was designed to collect socio-demographic data. Descriptive statistics, factor analysis, and linear regressions were used to analyze data. Results: < 0.05). Conclusions: Parents reported resilience that reflected their ability to cope with stressful events and mitigate stressors associated with having and caring for children with congenital heart disease. Lower education levels and lower gross household income are associated with lower resilience. To increase parents' resilience, nursing practice and nurse-led interventions should target screening and providing support for parents at-risk for lower resilience. As lower education level and financial hardship are factors that are difficult to modify through personal efforts, charitable foundations, federal and state governments should consider programs that would provide financial and health literacy support for parents at-risk for lower resilience.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".