Oral diadochokinetic rates across languages: Multilingual speakers comparison
Bibliographic record
Abstract
BACKGROUND: It is unclear whether oral diadochokinetic rate (oral-DDK) performance is affected by different languages within a multilingual country. AIMS: This study investigated the effects of age, sex, and stimulus type (real word in L1, L2 vs. non-word) on oral-DDK rates among healthy Malaysian-Malay speakers in order to establish language- and age-sensitive norms. The second aim was to compared the nonword 'pataka' oral-DDK rates produced by Malaysian-Malay speakers on currently available normative data for Hebrew speakers and Malaysian-Mandarin speakers. METHODS & PROCEDURES: Oral-DDK performance of 90 participants (aged 20-77 years) using nonword ('pataka'), Malay real word ('patahkan'), and English real word ('buttercake') was audio recorded. The number of syllables produced in 8 seconds was calculated. Mixed analysis of variance (ANOVA) was conducted to examine the effects of stimulus type (nonword, Malay, and English real word), sex (male, female), age (younger, 20-40 years; middle, 41-60 years; older, ≥61 years), and their interactions on the oral-DDK rate. Data obtained were also compared with the raw data of Malaysian-Mandarin and Hebrew speakers from the previous studies. OUTCOMES & RESULTS: A normative oral-DDK rate has been established for healthy Malaysian-Malay speakers. The oral-DDK rate was significantly affected by stimuli (p < 0.001). Malay real word showed the slowest rate, whereas there was no significant difference between English real word and nonword. The oral-DDK rate for Malay speakers was significantly higher than Mandarin and Hebrew speakers across stimuli (all p < 0.01). Interestingly, oral-DDK rates were not affected by age group for Malay speakers. CONCLUSIONS & IMPLICATIONS: Stimuli type and language affect the oral-DDK rate, indicating that speech-language therapists should consider using language-specific norms when assessing multilingual speakers. WHAT THIS PAPER ADDS: What is already known on the subject Age, sex, and language are factors that need to be considered when developing oral-DDK normative protocol. It is unclear whether oral-DDK performance is affected by different languages within a multilingual country. What this paper adds to existing knowledge No ageing effect across real word versus nonword on oral-DDK performance was observed among Malaysian-Malay speakers, contrasting with current available literature that speech movements slow down as we age. Additionally, Malaysian-Malay speakers have faster oral-DDK rates than Malaysian-Mandarin and Hebrew speakers across all stimuli. What are the potential or actual clinical implications of this work? Establishing normative data of different languages will enable speech-language therapists to select the appropriate reference dataset based on the language mastery of these multilingual speakers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".