Perspectives of Parents of Children with Special Educational Needs: Self-Efficacy and School Supports During COVID-19
Bibliographic record
Abstract
The role of parents in supporting at-home learning increased dramatically in the spring of 2020. Schools in most Canadian provinces closed physically due to COVID-19, and remote-learning options were quickly developed to ensure continued education for students. Many students with special educational needs, who typically benefit from a range of supports from school, became reliant on parents to provide means of access to and participation in remote learning. Using an online survey, we explored the perceptions of 263 Canadian parents of children with special education needs with regard to their self‑efficacy and supports from schools. We conducted multiple linear regression analyses for each of three dependent variables (academic supports, parent self‑efficacy, and social-emotional supports); independent variables included student grade level, education placement, and total school-provided supports prior to the pandemic. Findings indicated that most parents engaged in remote learning and lacked confidence in their ability to support the learning of their child. Parent self-efficacy was related to social-emotional supports from schools and not to academic supports. Parents of children in elementary grades, and of those who had received more supports from school prior to COVID‑19, reported feeling better supported in social-emotional areas by the school. Schools should explore ways of building strong collaborative relationships between educators and parents, as well as continuing to find ways of supporting families and students in both in- and out-of-school places. The pandemic, and school-building closures, have reminded us how partnerships between parents and schools are crucial for the well-being of all involved.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".