Adaptation and Norm Determination Study of the Boston Naming Test for Tukish Elderlys
Bibliographic record
Abstract
INTRODUCTION: The main purpose of this research is develop the Turkish version of the BNT long form (consist of 60 items) [BNT-60 (TR)] and to determine the normative data for Turkish healthy geriatric population. BNT is a neuropsychological test which was widely used to measure naming disorders associated with a variety of neuropathological events. This research consists of two stages. In the stage of pilot study, adaptation of test was completed and BNT-60 (TR) version was developed; and in the stage of normative study, normative data was collected and norm determination was completed. METHODS: Ninety healthy and volunteer elderly were participated in pilot study and 317 were in normative study. Three screening tests called Montreal Cognitive Assessment (MOCA), Functional Activities Questionnaire (FAQ) and Geriatric Depression Scale (GDS) were administered for participant selection. BNT-60 (TR) was applied to participants who meet the inclusion criteria. RESULTS: According to 5 (age) x 2 (gender) x 3 (education) factorial ANOVA results, main effects of age and education level on BNT-60 (TR) total score were found statistically significant. Then according to MANOVA results, main effects of age and education level on BNT-60 (TR) sub-scores were found statistically significant. On the other hand, main effect of gender was not significant on BNT-60 (TR) scores. The age and BNT-60 (TR) total score were negatively correlated. This results consistent with other normative studies of BNT in the literature. CONCLUSION: Finally, BNT-60 (TR) is adopted for Turkish culture, determined normative data and a test which is evaluating naming ability of the older adults was put into use.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".