Development of a Walking-Safety Scale for Older Adults, Part II: Interrater and Test–Retest Agreement of the GEM Scale
Bibliographic record
Abstract
PURPOSE: The GEM scale is an objective assessment tool, specifically developed for older adults, to evaluate walking safety using standardized tasks. The purpose of this study was to estimate the interrater and test-retest agreement of the GEM scale. METHOD: Participants (n = 41; >/= 65 years) were recruited from geriatric units and assessed independently and simultaneously by three raters on two occasions using the GEM scale. Kappa coefficients and percentage agreement were calculated for each item of the scale. RESULTS: A majority of walking items (n = 22) showed fair to substantial interrater agreement (kappa >/= 0.25) and substantial to almost perfect test-retest agreement (kappa >/= 0.60). Mean percentage agreement was high for both interrater and test-retest agreement (79% +/- 15% and 83% +/- 16% respectively). Moreover, detailed analyses demonstrated that the relatively low agreement of some items resulted from changes in the performance of some participants and the low variability of scores. Although some walking items showed less agreement, the final decision regarding the participants' ability to walk safely resulted in moderate to substantial interrater and test-retest agreement. CONCLUSION: The GEM scale is a new assessment tool that can now be used with estimated interrater and test-retest properties to allow therapists to objectively evaluate walking safety among the elderly.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".