Singing for the Rehabilitation of Acquired Neurogenic Communication Disorders: Continuing the Evidence Dialogue with a Survey of Current Practices in Speech-Language Pathology
Bibliographic record
Abstract
Therapeutic applications of singing (e.g., melodic intonation therapy) for acquired neurogenic communication disorders (ANCD) such as post-stroke aphasia, dysarthria, or neurodegenerative diseases have emerged from innovations by clinical speech-language pathologists (SLPs). However, these specialists have never been systematically consulted about the use of singing in their practices. We report a survey of 395 SLPs in France using an online questionnaire (September 2018-January 2019). Most (98%) knew that singing could be a therapeutic tool. A wide variety of uses emerged in our data. Some practices (e.g., song games) have not yet been investigated in research settings. Melodic therapy, which is supported by scientific evidence, is familiar to clinicians (90%), but they lack training and rarely follow a full protocol. Over half of respondents (62%) recognize group singing for various benefits, but do not often use it, mainly due to the lack of adapted or welcoming choirs in their area. These results provide key information for continued dialogue between researchers, clinicians, and the community. Considering the aging population and the associated increase in the prevalence of ANCD, access to group singing in particular could be facilitated for these patients from a social prescription perspective with further research evidence.
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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 | 0.027 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".