Barriers and facilitators to implementing prevent-teach-reinforce for young children in community-based early intervention services for autism
Bibliographic record
Abstract
The present study examined barriers and facilitators to the implementation of evidence-based practices to manage challenging behavior under real-world conditions within community-based autism services. Parents, early intervention practitioners, and administrators at a public agency who participated in Prevent-Teach-Reinforce for Young Children (PTR-YC), an intervention based on positive behavior support principles, shared their perspectives on the factors that helped or hindered the implementation of the program. Barriers and facilitators were identified at the macrosystemic (community), services (organization and program), and case (parent, family, child, practitioner) levels. Of note, the governmental response and lockdown measures of the Covid-19 pandemic highlighted both challenges and opportunities for the planned large-scale deployment of the intervention. While the program itself includes built-in facilitating elements (e.g., peer support, flexibility, efficiency), results also underscored the importance of robust change management practices, administrative support, responsive clinical supervision, and organizational commitment to professional development. Parents and practitioners, the core members of the intervention team, bring to bear many positive personal qualities but benefit from support in adopting the new approach and its procedures. • Evidence-based practices are challenging to apply in autism public services. • Stakeholders identified barriers and facilitators to program implementation. • Organizations need change management practices and effective leadership. • Parents are invested and empowered, but may require support and flexibility. • Practitioners can draw from experience and adapt, but need support and realistic work demands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".