Children Comorbidities Associated with High Parental Self-Efficacy: A Study on Parents of Children with Down Syndrome
Bibliographic record
Abstract
Background: Parental self-efficacy (PSE) in parents of children with Down syndrome (DS) refers to parents’ efficacy in their capability as parents nurturing their children with DS. Therefore, this study aimed to determine factors that may influence PSE in parents of children with DS. Methods: This cross-sectional study was carried out on 87 parents of children with DS. Self-Efficacy Parenting Task Index (SEPTI) was administered to measure PSE levels. Spearman’s rank correlation and independent t-test were applied to determine factors that may influence PSE. These include gender, children’s age, duration of being diagnosed with DS, comorbidities, number of children in the family, parents’ age, educational level, monthly household income, place of residence, genetic counseling, and support group. Results: The results showed that the median scores of SEPTI were 114 (101-143), and 57.5% of parents had moderate PSE levels. Significant factors affecting PSE include children with comorbidities (r = - 0.197, p = 0.033) and the place of residence (r = -0.212, p = 0.024). Parents of children with fewer comorbidities (119 (109-128), p =0.034), those with higher education (118 (110-132), p = 0.031), and those living in urban areas (115 (101-143), p = 0.025) demonstrated higher PSE. Additionally, multiple linear regression analysis showed children’s comorbidity as the most significant predictor in PSE (PR= 0.17, 95% CI = 0.03 - 0.77, p = 0.020). Conclusions: Children with comorbidities affect self-efficacy in parents of children with DS, besides the level of parent’s education and the place of living identity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".