Psychometric Properties of the Falls Efficacy Scale-International, Cut-off Points, and Validating its Short Version Among Iranian Older People
Bibliographic record
Abstract
Objectives: Older people have a fear of falling, which is far more difficult than falling itself. We measured the extent of this feeling using a short and valid Falls Efficacy Scale-International (FES-I) 7-item questionnaire for the aging community in Iran. Methods: The present psychometric work deals with outlining the validation and translation of FES-I (short version) among 9117 Persian-speaking elderly people with a mean age of 70.2±8.3 years (54.1% female and 45.9% male) in July 2021. Investigations were performed on confirmatory factor analysis, exploratory factor analysis, internal consistency, and construct validity along with test-retest reliability, receiver operating characteristic analysis, inter-rater, and convergent validity. Results: 72.4% of the subjects were living alone, 92.9% required support in activities of daily living , and 93.0% experienced falling in the past two years. A one-factor solution was assigned by exploratory factor analysis for FES-I. Thus, this model was proved by the confirmatory factor analysis with valid fit indices. Based on Cronbach’s alpha, intra-cluster correlation coefficient, and McDonald’s omega (≥ 0.80), internal consistency was confirmed. The exact cut-off value was represented by the receiver operating characteristic analysis for male/female and between with/without fear of falling among older samples with higher measures of specificity and sensitivity. Moreover, a significant effect of age, aging in place, loneliness, hospitalization rate, frailty, and sense of anxiety (effect size ≥ 0.80, p ≤ 0.05) on fear of falls was detected using analysis of variance.Conclusions: The psychometric properties of the original scale were preserved by the Persian version of FES-I seven items as a self-reported measure of fear of falling. It could be assuredly a measure in both community and clinical settings. The possible uses and limitations of the Iranian FES-I were also discussed.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".