Consequences of Coronavirus as a Predictor of Emotional Security among Mothers of Children with Intellectual Disabilities
Bibliographic record
Abstract
Parents of children with intellectual disability (ID) suffer from comparatively emotional insecurity compared to parents of children without ID, especially during periods of crisis, such as the outbreak of coronavirus pandemic. This study aimed to investigate the consequences of Coronavirus as a predictor of emotional security among mothers of children with intellectual disabilities. Mothers of children with intellectual disabilities were considered for data collection. The study group of the research consists of 120 mothers who have children with intellectual disabilities. They aged 23-55 years, (M= 43.23, SE=0.075). Consequences of Coronavirus Questionnaire (CCQ-20) and Emotional Security Scale (ESS-20) were used to collect and analyze data. For this study, quantitative survey research was employed. The independent variable is the consequences of Coronavirus, and emotional security is the dependent variable. The independent variable is the consequences of Coronavirus, and emotional security is the dependent variable. To test the hypotheses of the study, Pearson correlation and Linear regression analysis were conducted. Findings indicate that the independent variable (Coronavirus) contributed to the prediction of emotional security among mothers of children with intellectual disabilities. Findings of the current study indicate that the greater the Consequences of Corona, the less emotionally secure the mother is likely to feel during the COVID-19 lockdown in Saudi Arabia.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".