Development and validation of a modified falls-efficacy scale
Bibliographic record
Abstract
PURPOSE: This study examined the psychometric properties of a modified falls-efficacy scale (FES) that included more challenging activities of daily living items and made reference to the presence or absence of enabling assistive devices that are part of the built environment. METHOD: Baseline data from a longitudinal study among a cohort of 551 community-living seniors was used to generate data to inform the current report. Data for this study was collected in seniors' homes and apartments in two neighbouring cities in Canada, Ottawa and Gatineau. Measurements included a modified falls self-efficacy scale, various health and demographic measures. RESULTS: Factor analysis of the instrument revealed a two-factor solution, explaining 60.3% of the variance. The two emerging subscales were: Subscale 1--basic activities of daily living (ADLs), and subscale 2--challenging ADLs. The modified FES demonstrated greater internal consistency and better response variability than Tinetti's original FES. CONCLUSIONS: Adding more challenging ADL items and specifying use of assistive devices while undertaking the ADL may increase the FES' ability to distinguish between participants with varying degrees of mobility or health impairment. Recommendations for future research are offered and implications for use are 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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".