Looking back and moving forward: A scoping review of research on preschool autism interventions in the field of speech-language pathology
Bibliographic record
Abstract
Background & Aims: Speech-language pathology services are frequently accessed by families of children who have suspected or diagnosed autism. This is expected given that social communication differences are a core feature of autism. This review looked broadly at the state of research in the field of speech-language pathology and preschool autism interventions in order to identify the types of studies that could be used to inform the practices of speech-language pathologists (SLPs), and to identify gaps in the field so they can be addressed in future research. Specifically, we examined the extent of research conducted on interventions delivered (at least in part) by SLPs to preschool children with suspected or diagnosed autism, identified the range of skill development areas targeted within the studies, and explored the characteristics of the interventions (i.e., theoretical models underlying the programs, service delivery models, treatment dosage). Methods: A scoping review of articles published between 1980 and 2019 was conducted using the five phases outlined by the Arksey and O'Malley framework: (a) articulating the research question; (b) identifying relevant studies; (c) selecting studies; (d) charting the data; and (e) collating, summarizing, and reporting the results. Main Contribution/Results: A total of 114 studies met inclusion criteria with most published since 2010 and conducted within North America. Case study or single-subject study designs were the most frequently used. Interventions delivered solely by SLPs and by multiprofessional teams that included SLPs were relatively equally represented. Across the included studies, nine skill development areas were targeted, but interventions targeting social communication, language, and augmentative communication skills made up the vast majority of studies. There was relatively even distribution of interventions informed by child-centered, clinician-directed, and hybrid models. Explicit information detailing intervention characteristics (e.g., treatment dosage, professional training of clinicians delivering the intervention) was poorly reported in many studies. For those studies providing details, there was a great deal of variability in the nature of interventions (e.g., service delivery models, SLPs' role, dosage). Conclusions: This review revealed that research in the area of autism interventions delivered, at least in part, by SLPs has markedly increased over the past 10 years. Still, there remains a need for more research, and greater transparency detailing the nature of the interventions being investigated. The research conducted to date captures the versatility of the SLP's role within preschool autism intervention. Improved reporting and studies with strong methodological rigor focused on capturing the complex and individualized nature of interventions are needed, as are intervention studies aligned with real-world community practice. Implications: This review provides a comprehensive examination of the status of research on preschool interventions delivered to children with suspected or diagnosed autism within the field of speech-language pathology. Several directions for future research are provided, as are suggestions for improving the clinical applicability of results to further the development of effective, evidence-informed policy and practice in speech-language pathology.
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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 | 0.040 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.038 | 0.034 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".