Telehealth intervention and childhood apraxia of speech: a scoping review
Bibliographic record
Abstract
Background: In the wake of the COVID-19 outbreak, many speech-language pathologists have transitioned from in-person service delivery to online environments. As such, there is an urgent need to inform clinicians on the availability of efficacious and effective telehealth interventions for childhood apraxia of speech (CAS).Objectives: This review was informed by the following clinical question: Is providing intervention remotely through telehealth as efficacious and effective as in-person therapy for treating CAS?Methods, eligibility criteria, and sources of evidence: Eight databases and seven search engines were searched for articles to identify intervention studies that have investigated the efficacy and/or effectiveness of treating CAS remotely. Search criteria was restricted to papers with children under 18 years of age, published in the English language between 1993 and 2020.Results: Two studies were found to meet our inclusion criteria. A phase I study employed a multiple baseline across participants design to investigate the efficacy of the Rapid Syllable Transition treatment via telehealth. The second study assessed the feasibility of adopting a novel system for the remote administration of the Nuffield Dyspraxia Program-Third Edition. Based on the Oxford hierarchy Centre for Evidence-Based Medicine, both studies are level IV (case-series/case-control), and therefore deemed low level evidence. Results showed limited but promising outcomes when CAS therapy is conducted remotely.Conclusion: There is limited, low-level evidence indicating positive outcomes for the remote treatment of CAS via telehealth. The scarcity of data available warrants a need for large-scale randomized control trials and controlled clinical trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".