Adaptation and normative data of the cognitive assessment battery of language (BECLA-Tr) for the Turkish adult population
Bibliographic record
Abstract
Introduction Normal cognitive aging is identified from pathological through the administration of neuropsychological tests, including tests of language ability. Compared to English, available tests and test batteries for assessing acquired language deficits in Turkish speakers are much more limited. This study reports on the adaptation and generation of normative data of the The Batterie d’Évaluation Cognitive du Langage (BECLA) for the adult Turkish population.Method The article describes two studies. Study 1 concerns the Turkish adaptation of the BECLA (BECLA-Tr). Study 2 examined a group of older neurotypical Turkish-speaking adults to obtain normative data.Results In Study 1, some changes were made to adapt the original test battery linguistically and culturally for use in Turkey. In Study 2, normative data were obtained based on the performance of 409 participants aged 18 years and older with different education levels.Conclusion The BECLA-Tr fills an important gap in the clinical practice of speech and language pathologists in Turkey. This comprehensive test battery has the potential to help clinicians and researchers better detect acquired language deficits in the adult and elderly Turkish population.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".