Parental Self-Efficacy During COVID-19: Parents’ Experiences Supporting the Learning of their Child(ren) with Special Educational Needs
Bibliographic record
Abstract
Schools closed as a result of the COVID-19 pandemic with the expectation that learning continue from home. This presented a unique challenge for parents of children with special educational needs as during this time levels of stress were high and access to supports were low. The purpose of this mixed methods study was to explore and describe the experiences of Canadian parents of children with SEN with at-home learning as it related to their learning-specific parental self-efficacy (L-PSE), perceived stress and perceived support from their child’s school. Quantitative analysis revealed that L-PSE was significantly and negatively related to perceived stress. Parents did not differ in their perception of school supports. While qualitative analysis identified many similarities across groups, it also highlighted negative experiences being described more often by parents with low L-PSE. Overall, the findings of this study provide evidence that parents with high and low parental self-efficacy differ in their experiences of supporting the learning of their children with SEN and that efficacy was related to the overall experience that parents had during COVID-19. This study serves to add to the limited body of literature on L- PSE, as well as inform the efforts of schools and other professionals in supporting the parents of children with SEN and their families.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".