Item Response Theory of the English Version of the Falls Efficacy Scale-International Tool Among Community-Dwelling Older Adults From Four Different Sites
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The Falls Efficacy Scale-International (FES-I) is widely used in clinical practice, but it is unclear how each item can discriminate different levels of fall concern. This study applied item response theory (IRT) to evaluate the psychometric properties of each item in the original English version of FES-I among older adults, and compared those properties across genders and with translations in French, Portuguese, and Spanish. METHODS: This cross-sectional study used data from the International Mobility in Aging Study, which recruited community-dwelling older adults. Modified graded IRT was used to assess the psychometric properties of the FES-I items, specifically estimating difficulty and discrimination (ie, ability to differentiate levels of fall concern) parameters. Item reliability across the different levels of fall concern was estimated and differential item functioning (DIF) was tested for each item to assess if participants perceived the items similarly regardless of gender and language. RESULTS AND DISCUSSION: The study included 1608 community-dwelling older adults, of which 395 had completed the English version of the FES-I. Generally, the IRT results showed that the English version was a reliable tool, especially for older adults with high fall concerns, but did not distinguish between low and moderate levels of concern. Factor analysis supported the construct validity of the FES-I. In the DIF analysis, 2 items were perceived differently by gender in the English version, and 9 items were perceived differently between the English and the translated versions (French, Spanish, and Portuguese). The presence of DIF indicates that the psychometric properties of these items are different across genders and languages, and they might relate to cultural factors, the surrounding environment, the wording, the biological differences between men and women, and the item's task itself. CONCLUSIONS: The FES-I is a reliable and valid scale for identifying older adults with high fall concern, but it should include more difficult items. Additionally, differing perceptions of items across genders and languages necessitate caution in comparing results among diverse populations.
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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 | 0.032 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".