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Record W4312373054 · doi:10.47611/jsr.v11i2.1562

Stuttering Treatment Approaches from the Past Two Decades: Comprehensive Survey and Review

2022· article· en· W4312373054 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Student Research · 2022
Typearticle
Languageen
FieldPsychology
TopicStuttering Research and Treatment
Canadian institutionsUniversity of AlbertaUniversity of Waterloo
Fundersnot available
KeywordsStutteringPsychological interventionFluencyPsychologyIntervention (counseling)Applied psychologyMedicineDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

This comprehensive survey and review presents stuttering treatment approaches that have been reported in the past 20 years in order to highlight the different characteristics in each intervention. The comprehensive survey presented in this article was conducted according to the PRISMA guidelines to extract articles on stuttering interventions, published between 01/01/2000 and 01/08/2020. 11 formal programs, 9 fluency induction techniques and 7 adjunct therapy approaches were identified through the comprehensive survey and summarized. The most common results were the Lidcombe program and altered auditory feedback techniques. The comprehensive survey and review presented in this article strives to provide knowledge that can help researchers in other areas, such as Human-Robot Interaction (HRI), acquire a preliminary understanding of stuttering interventions and further the field of stuttering interventions with the introduction of technological advancements.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.565
GPT teacher head0.535
Teacher spread0.030 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it