Development and Validation of a Psychological Resilience Scale for Mothers of Children with ASD: Calibration with Rasch-Andrich Model
Bibliographic record
Abstract
Background: The present study aimed to develop and validate a psychological resilience scale for mothers of children with ASD using calibration with the Rasch-Andrich model. Methods: A quantitative approach of national survey research design using an online questionnaire was applied. The cross-sectional study involves a sample of mothers of children with ASD in KSA. Purposive sampling was employed. There were 310 mothers of children with ASD. All of them filled in all questions on the scale. Findings: Concerning unidimensionality and local independence assumptions, the first factor explains more than (20%) of the variance in score with respect to the total variance due to all factors, so it can be said that there is one factor behind the items of the scale. The separation index and reliability of the psychological resilience scale for mothers of children with ASD were at an acceptable level. The person separation reliability was 0.96, indicating an acceptable degree of confidence in replicating the placement of persons within measurement error. Conclusion: Analysis by Item Response Theory (IRT) models allows a considerably deeper understanding of the psychometric properties of the items and scale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.002 |
| 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".