Investigation of the validity, reliability and psychometric properties of the Turkish version of the Ottawa sitting scale in patients with Parkinson’s disease
Bibliographic record
Abstract
The Ottawa Sitting Scale is a tool for the multidimensional assessment of sitting balance. This study aimed to investigate the validity, reliability, and psychometric properties of the Turkish version of the Ottawa Sitting Scale (OSS-TR) in Turkish-speaking patients with Parkinson's disease (PD). The study included 56 patients diagnosed with PD based on the UK Brain Bank Criteria. Construct validity of the OSS-TR was established through the evaluation of structural and convergent validity. Explanatory factor analysis and confirmatory factor analysis (CFA) were carried out to determine the structural validity. Convergent validity was analysed by examining the relationships between OSS-TR with the Berg Balance Scale (BBS) and Trunk Impairment Scale (TIS). The test-retest reliability of the scale was assessed by intraclass correlation coefficient (ICC) and internal consistency was assessed by Cronbach's alpha coefficient. The good fit determined according to the model fit criteria based on the CFA results confirmed the structural validity. Furthermore, the high associations between OSS-TR with the BBS (r = 0.766) and TIS (r = 0.720) supported convergent validity (p < 0.05). Test-retest reliability of the OSS-TR was excellent (ICC = 0.867). Moreover, internal consistency was high (Cronbach's alpha = 0.948). The OSS-TR is a valid and reliable instrument for assessing sitting balance in Turkish-speaking PD patients. Regarding the results of the study, OSS-TR can be considered useful in the evaluation of sitting balance among PD patients in clinical and research settings.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".