Emotional coping strategies in children with and without special educational needs during the COVID-19 pandemic in Saudi Arabia
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic significantly disrupted children's daily lives, especially those of children with special educational needs and disabilities (SEND). This study aimed to compare the coping strategies of children with SEND to those of typically developing (TD) peers, as reported by their parents, and to identify the factors associated with coping efficacy early during the COVID-19 pandemic. Methodology: We conducted a nationwide cross-sectional survey between May and July 2020 using the Arabic translation of a global project's survey. Participants were recruited from all regions of Saudi Arabia through text messages sent to beneficiaries of the Ministry of Human Resources and Social Development, the Autism Center of Excellence, and the Authority for Persons with Disabilities. Parents of 548 pairs of SEND and TD children, matched by age (±3 years), completed the survey and were included in the analysis. Coping strategies were analyzed and grouped into adaptive and maladaptive factors. Results: < 0.001) for all reported coping strategies. Multiple factors were associated with higher odds of adaptive coping, including higher parental educational level, children's anxiety levels at the start of the pandemic, and their awareness of COVID-19. These factors were similarly associated with higher maladaptive coping and coping efficacy. Conclusion: TD children utilized a larger repertoire of coping strategies and had greater coping efficacy compared to peers with SEND early during the pandemic. These findings emphasize the need for targeted, community-level interventions to promote coping in children with SEND, particularly during pandemics and other public health crises.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".