Uncovering the Moment-to-Moment Processes and Strategies of Staff in School-Based Day Treatment for Children with Social, Emotional and Behavioural Difficulties
Bibliographic record
Abstract
The school-based day treatment program at the Child Development Institute (CDI) provides intensive mental health services for children in kindergarten and grade 1 with social, emotional and behavioural difficulties who are unable to manage in mainstream classrooms. Despite services such as this having been offered in Canada for several decades, research on day treatment is scarce with no standard models for program delivery. Using stimulated recall interviews with classroom staff, this study aimed to describe the moment-to-moment processes and strategies of classroom staff in the day treatment program at CDI and incorporate them into a preliminary program model. Several processes and strategies used by staff members emerged from thematic analysis of the interviews. These included a process of tailored intervention, characterized by a continual and cyclical process of attunement, responsiveness, assessment and evaluation, using a team-based approach, noticing positives about children, a climate of positive relationships, staff regulating their own emotions, being flexible while also being firm and consistent, and seeing children from a developmental perspective. More specific strategies used by staff members (e.g., token economy) also emerged from the interviews. School readiness was identified as being the major developmental goal for children in the day treatment program. Ultimately, the program was conceived as a milieu therapy. Implications for future research and preservice teacher training are discussed.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".