A scoping review of the school-aged stuttering intervention literature
Bibliographic record
Abstract
We explore school-aged (6–14 years old) stuttering interventions for children who stutter (CWS) using a scoping review. Database searches were conducted (EBSCO host, PubMed, PsycINFO, Academic Search Premier, MEDLINE, Education Resources Information Center (ERIC), Health Source (consumer edition), Africa-wide Information, Cumulative Index to Nursing and Allied Health (CINAHL), Dissertation abstracts International, the Cochrane Library (Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials (CENTRAL), 15 Cochrane Methodology Register, Education Resource Information Center, Google Scholar, manual searching using reference lists and gray literature) from the inception of the databases until June 2018. Ten school-aged stuttering intervention studies met the inclusion criteria of this study, which were conducted between 1982 and 2016. The 10 studies used quantitative pretest posttest designs with low to moderate quality, according to GRADE. ICF showed a dominance of interventions targeted and measured within the domain of body structures and function with treatment effects focused predominantly on traditional individualized speech fluency measures. Studies were conducted in Australia (n = 3), United States of America (n = 3), Canada (n = 2), Islamic Republic of Iran (n = 1) and South Africa (n = 1). The findings lead to the authors questioning knowledge production and its influence on evidence-based literature and practices.
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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.029 | 0.026 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".