Validity and reliability of the Turkish version of “The Occupational Circumstances Assessment Interview and Rating Scale” in rheumatic diseases
Bibliographic record
Abstract
Introduction: Rheumatic diseases significantly impact daily activities, emphasizing the need to understand their occupational profiles. Identifying these profiles using the Occupational Circumstances Assessment Interview and Rating Scale can aid in tailoring treatment approaches. This study aimed to evaluate the validity and reliability of the Turkish version of Occupational Circumstances Assessment Interview and Rating Scale in rheumatic diseases. Method: The scale’s internal construct validity was examined using the Rasch measurement model. Convergent validity of Turkish version of Occupational Circumstances Assessment Interview and Rating Scale was evaluated through Spearman’s correlation coefficient, assessing associations with the Community Integration Questionnaire, World Health Organization Disability Assessment Schedule, Second Version and Canadian Occupational Performance Measure. Results: Correlation analyses demonstrated a positive correlation between Turkish version of Occupational Circumstances Assessment Interview and Rating Scale Rasch-transformed scores and Canadian Occupational Performance Measure total ( r = 0.208; p = 0.009), along with negative correlations with Community Integration Questionnaire ( r = −0.210; p = 0.008) and World Health Organization Disability Assessment Schedule, Second Version ( r = −0.539; p < 0.000). Conclusion: Findings confirm Turkish version of Occupational Circumstances Assessment Interview and Rating Scale as a valid and reliable tool for assessing the occupational profiles of individuals with rheumatic diseases. It can guide treatment plans and help develop effective strategies to enhance daily life participation.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".