Exploring Children’s Perceptions of an After-School Canine-Assisted Social and Emotional Learning Program: A Case Study
Bibliographic record
Abstract
This study explored children’s perceptions of a canine-assisted social-emotional learning program developed within the framework of a canine therapy program at a mid-sized Canadian university. Data collection made use of interviews, field notes, and observations. Children (N = 8, 5 – 11 years) from an after-school program participated in a six-week intervention after which participants were interviewed about their experiences in the program, their learning of social and emotional competencies, and the role of the therapy dogs in facilitating their socioemotional development. Using conventional content analysis, salient themes reflecting participants’ experiences were identified. A within-case analysis was conducted followed by a cross-case analysis to identify what participants collectively saw as important. Salient themes to emerge through cross-case analysis were: 1) the dogs were meaningful and essential to the program, 2) it was an enjoyable and positive experience, and 3) participants reported evidence of social-emotional learnings. Evidence from this study suggests that the therapy dogs might have provided behavioral and emotional support. Findings suggest that integrating therapy dogs into social and emotional learning initiatives can provide unique advantages and improve children’s engagement and learning of social and emotional skills. Findings are discussed within the context of human-animal interactions and social and emotional education.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".