Current Practices for Evaluation of Resonance Disorders in North America
Bibliographic record
Abstract
Improving treatment outcomes for people with resonance problems (due to velopharyngeal disorders) is a priority for many speech-language pathologists (SLPs), but there exists a limited understanding of the practices SLPs are using to assess and monitor therapeutic effects in this population. The current study was designed to answer the following questions: (1) What are current clinical practices versus best practices for assessing resonance disorders, tracking therapeutic effects, and determining discharge criteria? (2) What assessment practices would SLPs prefer to use with clients who have resonance disorders? (3) What are barriers to SLPs' use of best practices? and (4) What effects do SLP demographics have on clinical practices? Thirty-eight SLPs, specializing in the treatment of resonance disorders, participated in the study. Responses were compared with best practice recommendations derived from the literature. Most clinicians were using low-tech assessment tools, often because they lacked access to high-tech tools. Demographics and training did not affect clinical assessment practices. There is a need to increase the availability of high-tech assessment tools to SLPs practicing in the area of resonance disorders, as consistent use of sophisticated assessment devices would exemplify contemporary thinking about the transfer of knowledge to practice in this area.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".