Fidelity to the ACT SMART Toolkit: an instrumental case study of implementation strategy fidelity
Bibliographic record
Abstract
BACKGROUND: Evidence-based practices (EBPs) are shown to improve a variety of outcomes for autistic children. However, EBPs often are mis-implemented or not implemented in community-based settings where many autistic children receive usual care services. A blended implementation process and capacity-building implementation strategy, developed to facilitate the adoption and implementation of EBPs for autism spectrum disorder (ASD) in community-based settings, is the Autism Community Toolkit: Systems to Measure and Adopt Research-based Treatments (ACT SMART Toolkit). Based on an adapted Exploration, Adoption decision, Preparation, Implementation, Sustainment (EPIS) Framework, the multi-phased ACT SMART Toolkit is comprised of (a) implementation facilitation, (b) agency-based implementation teams, and (c) a web-based interface. In this instrumental case study, we developed and utilized a method to evaluate fidelity to the ACT SMART Toolkit. This study responds to the need for implementation strategy fidelity evaluation methods and may provide evidence supporting the use of the ACT SMART Toolkit. METHODS: We used an instrumental case study approach to assess fidelity to the ACT SMART Toolkit during its pilot study with six ASD community agencies located in southern California. We assessed adherence, dose, and implementation team responsiveness for each phase and activity of the toolkit at both an aggregate and individual agency level. RESULTS: Overall, we found that adherence, dose, and implementation team responsiveness to the ACT SMART Toolkit were high, with some variability by EPIS phase and specific activity as well as by ASD community agency. At the aggregate level, adherence and dose were rated notably lowest during the preparation phase of the toolkit, which is a more activity-intensive phase of the toolkit. CONCLUSIONS: This evaluation of fidelity to the ACT SMART Toolkit, utilizing an instrumental case study design, demonstrated the potential for the strategy to be used with fidelity in ASD community-based agencies. Findings related to the variability of implementation strategy fidelity in the present study may also inform future adaptations to the toolkit and point to broader trends of how implementation strategy fidelity may vary by content and context.
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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 | 0.064 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".