Validation of the Persian version of the 9-item Berg Balance Scale Among Older Iranians
Bibliographic record
Abstract
Objectives: Old age is often associated with a progressive decline in the capacity of individuals to maintain dynamic and static balance, leading to falls and fear of falling. This study aimed to validate the 9-item Berg Balance Scale (BBS-9) for the older Iranian population. Methods: The current psychometric study involved translation of the BBS-9 to Persian language and its validation among a cohort of Persian-speaking elderly people. Confirmatory factor analysis, exploratory factor analysis, internal consistency, construct validity, test-retest reliability, receiver operating characteristic analysis, inter-rater, and convergent validity of the BBS-9 (Persian) were investigated and statistically analyzed. Results: The participants were 9117 Iranians with an average age of 64.3±2.45 years. The cohort was 54.1% female. Nearly three quarters of the subjects (72.4%) lived alone, 92.9% needed help with activities of daily living, and 93.0% sustained falls in the previous two years. Internal consistency was confirmed using intraclass correlation coefficient and McDonald’s Omega (≥ 0.75). The receiver operating characteristic analysis represented the exact cut-off values for male and female and with or without fear of falling with good specificity and sensitivity. Analysis of variance revealed that fear of falling was significantly related to age, Aging in Place, loneliness, hospitalization rate, frailty, and sense of anxiety (effect size ≥ 0.130, p ≤ 0.050). Conclusions: The Persian version of BBS-9, a psychometrically sound self-reported measure of fear of falling, retained the original’s satisfactory psychometric properties. It has the potential to be used among older Iranians in community-based and clinical 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".