Assessing a Novel Implementation Support Package for Teachers’ Use of the Daily Report Card: A Case Study
Bibliographic record
Abstract
Supporting the implementation of evidence-based interventions is a core goal of implementation science. Although prior research points to the role of technology and intervention coaches in supporting implementation in the school setting, existing models may not be feasible in many real-world school contexts, due to time and resource constraints. Thus, the purpose of this study was to examine the acceptability, feasibility, and utility of a novel implementation support package that leverages interactive technology and strategic resources within the existing school social network to support teachers’ use of the Daily Report Card. The Daily Report Card is an evidence-based intervention for disruptive classroom behavior. We used a multi-method single case study to understand this implementation support package in one school district in western Canada. Data for this study included social network analysis, teacher and coach surveys, a coach focus group, teacher interviews, and teacher implementation data. Results indicate that our implementation support package was viewed as acceptable, feasible, and useful by participating schools, but that some improvements are also needed. Lessons learned about leveraging peers within the social network and about the use of interactive technology to support implementation 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 | 0.028 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".