Parental Factors Related to COVID-19 Prevention Behavior in Children with Intellectual Disability: Partial Least Square Structural Equation Modeling
Bibliographic record
Abstract
Efforts to prevent COVID-19 in children with intellectual disability require the role of parents. Even though the vaccine has been implemented, the most important effort is to implement health protocols. Implementing health protocols cannot be separated from knowledge, attitudes, intentions, subjective norms, and social support from parents. This research aims to determine the influence of knowledge, attitudes, intentions, subjective norms, and social support on the COVID-19 prevention behavior of parents of children with intellectual disability. This type of research is descriptive correlational research, and the developed model is validated using the partial least squares structural equality modeling (PLS-SEM) approach based on data collected from 100 parents of children with intellectual disabilities taken using purposive sampling at Semarang Municipal Special Schools.The study results show that parental characteristics, namely education, influence attitudes, which can ultimately affect parental intentions. Parental education also affects social norms, namely social support and subjective norms, which can determine COVID-19 prevention behavior. Parental education is a priority in public health strategies because it can directly shape attitudes, intentions, and social norms that can improve the health of children with intellectual disabilities. Health programs and education for parents must be focused and carried out consistently and continuously.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".