Modified School-Based Facing Your Fears for Students with Autism Spectrum Disorder and Anxiety: A Preliminary Quasi-Experimental and Qualitative Analysis
Bibliographic record
Abstract
With a high prevalence of anxiety among children with Autism Spectrum Disorder, researchers have identified implementing effective treatments in real-world settings as a priority. In the present study, multiple methods were employed to evaluate the effectiveness of educators delivering a modified school-based Facing Your Fears Cognitive Behavior Therapy program to treat anxiety among students with Autism Spectrum Disorder. Participants included 5 students, 6 educators and 7 parents. Settings were a public elementary school and private school. Multiple methods included a quasi-experimental group design, time series design, semi-structured interviews and focus groups. Quantitative results indicated that with training and on-going feedback educators obtained a modest level of fidelity in implementing the intervention. Non-significant decreases in student anxiety symptoms across all informant ratings (students, parents and teachers) were observed. Social validity ratings across participants were high. Qualitative findings revealed the following themes: (a) outcomes; (b) program structure; (c) inclusion; and (d) factors critical for success. These preliminary results offer a model for researchers to collaborate with key stakeholders in adapting interventions for use in schools, bridging the gap between research and practice. They highlight educator’s ability to deliver empirically-supported treatments to address anxiety among students with Autism Spectrum Disorder.
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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 | 0.019 | 0.024 |
| 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 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".