Proceedings from the 11th Annual Conference on the Science of Dissemination and Implementation
Bibliographic record
Abstract
Background: The growing number of students with autism has resulted in a proliferation of computer-assisted interventions (CAI) to increase treatment access.There is little rigorous study of how introducing this new technology affects teachers' use of existing evidence-based practice (EBP).Teachers may, inaccurately, see CAI as replacing EBP, and therefore deimplement EBP.Conversely, they may use CAI to implement EBP with some students while others are occupied on the computer.We conducted a mixed methods study to examine the effects of introducing CAI on teachers' use of EBP.Methods: We conducted a randomized field trial of one CAI, Teach-Town, and found that it was not effective in improving student outcomes.We then examined how its implementation affected teachers' use of two one-to-one EBP and one classroom-wide EBP, visual schedules.Seventy-three classrooms were randomized to TeachTown or control.All classrooms received ongoing training and coaching in the three EBP prior to and throughout the trial.Teachers' EBP use was measured monthly.Hierarchical models were used to test changes in EBP use over the year.Semi-structured interviews were conducted with teachers in the TeachTown condition at the end of the year, using a modified grounded theory approach, to explain the quantitative findings.Findings: During the course of the school year, teachers in the control group reported significant increases in their use of one-to-one EBP (p's < .05).While teachers in the TeachTown group showed higher use of these practices at baseline, their use decreased slightly during the course of the year, such that growth trajectories differed significantly between the groups (p's < .05).There were no differences in use of visual schedules.In qualitative analysis, teachers revealed that they thought that TeachTown was more effective and much easier to use than one-to-one instruction, was appealing to students and parents, addressed staffing shortages, and helped teachers manage challenging behaviors.Implications for D&I Research: New practices, introduced with the best intentions, can unintendedly result in deimplementation of other EBP that they were not meant to replace.Practitioners' perceptions of intervention characteristics may trump information about effectiveness, with ease of use, consumer appeal, and ability to address urgent concerns being most important.
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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 | 0.056 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.153 | 0.062 |
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".