Reliability and Validity of the Korean-Parental Self-Efficacy with Eczema Care Index
Bibliographic record
Abstract
BACKGROUND: Atopic dermatitis is a global problem affecting children, and its prevalence in Korea is steadily increasing. Since it is a chronically relapsing inflammatory skin disease, caregiver management of young children's atopic dermatitis is crucial for positive treatment outcomes. A factor that contributes to adherence to recommended prescriptions is parents' self-efficacy. However, accurate measurements of parental self-efficacy in relation to disease-specific task management are scarce. OBJECTIVES: This study examined the psychometric properties of the Korean language version of the Parental Self-Efficacy with Eczema Care Index (K-PASECI). METHODS: One hundred twenty five mothers of children younger than 13 years old who had atopic dermatitis were recruited from three tertiary hospitals across Korea. The K-PASECI was developed in accordance with the published guidelines. Psychometric testing included factor analysis, internal consistency testing, and concurrent validity analysis by comparing K-PASECI domains with parenting self-efficacy subscales. RESULTS: Factor analysis revealed a four-factor structure that explained 69.4% of the variance. The four factors were as follows; managing a child's symptoms and behaviour, communicating with medical staff, managing medication, and using moisturizer as part of routine management. The findings showed acceptable internal consistency (α=.94) and a moderate positive correlation with parenting self-efficacy (r=.48, p<.001). CONCLUSION: The K-PASECI, a reliable and valid scale for measuring self-efficacy in parents caring for children with atopic dermatitis, may be used in clinical and research settings to measure parents' self-efficacy in Korea, as well as in other English-speaking countries.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".