Overview of Evidence-Based Treatment of Stuttering in Bulgaria and in the USA and Canada
Bibliographic record
Abstract
Introduction: The move towards evidence-based practice (EBP) requires speech-language pathologists (logopedists) to understand the types of studies that build an evidence base for the field as well as the standards for assessing the quality of evidence. \nObjectives: This article discusses the conceptual and methodological issues associated with EBP in Bulgaria, the USA, and Canada related to clinical stuttering intervention. This article discusses how the movement towards the current high-level standards of practice established in North America may challenge some of the traditional Bulgarian beliefs regarding stuttering treatment. \nMethod: Theoretical overview and analysis of the existing literature data sources including systematic meta-analysis articles on EBP on stuttering. \nResults: The study outlines the steps of EBP accepted in SLP. Thirteen systematic reviews and meta-analyses are discussed concerning the application of research evidence to clinical decision making. These issues remain problematic for Bulgarian logopedists who received their training before or during the recent professional shift from a special education subspecialty to a health profession specialty as represented by speech-language pathology (SLP). \nConclusions: The professional bodies that govern clinical practice in the Bulgarian health fields are not currently guided by EBP concepts. In Bulgaria, there is no evidence-based framework for the prevention, diagnosis, and treatment of stuttering as there is in the USA and Canada. In Bulgaria EBP stands as a fundamental way to promote changes appropriate for SLP as a health profession.
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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 | 0.026 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.041 | 0.051 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".