The Effects of the Better Emotional and Social Times Program on Emotional and Social Skills Associated With Children With Learning Disabilities
Bibliographic record
Abstract
Children with learning disabilities are not only falling behind academically, they are also falling behind emotionally and socially, which is extremely important. This places a strong emphasis on a need for early intervention programs for children with learning disabilities to address these emotional and social deficits. The purpose of this study was to assess the effectiveness of the Better Emotional and Social Times (B.E.S.T.) program offered by the Learning Disabilities association of Niagara Region (LDANR), which targeted areas of self-advocacy, self-esteem, self-understanding (mindfulness), emotional regulation and social skills. Seven children between the ages of 6 and 11 enrolled in the program were closely observed for eight weeks and pre- and post- child interviews and observational checklists were completed throughout the duration of the program by the facilitators as well as the parent/caregivers. Results indicated that all of the children had improved and gained new skill sets in all of these areas. Overall, these results have positive implications, as this type of intervention can be implemented within education policy to ensure children with learning disabilities are receiving the proper aid in regards to these emotional and social deficits, which will also have a positive impact on their literacy deficits as well.
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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 | 0.001 | 0.002 |
| 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".