Test-retest reliability, internal consistency, construct validity and factor structure of a falls risk perception questionnaire in older adults with type 2 diabetes mellitus: a prospective cohort study
Bibliographic record
Abstract
Abstract Background Older adults with type 2 diabetes (DM2) are at increased risk of falling due to complications including: diabetic peripheral neuropathy, diabetic retinopathy, autonomic neuropathy and diabetic foot ulcers. The purpose of this study was to determine the test-retest reliability, internal consistency, construct validity and to perform factor analysis of a new falls Risk Perception Questionnaire (RPQ) in older community-dwelling adults with DM2. Methods A prospective cohort of 30 community-dwelling older adults, ≥ 55 years, with DM2 was assembled. At baseline, perceived risk of falling, fear of falling and physical activity were measured. At time 2 (T2), at least 2 days later, perceived risk of falling was assessed again to determine the test-retest reliability of the RPQ. At time 3 (T3), approximately six weeks later, and time 4 (T4), at least 2 days after T3, perceived risk of falling was assessed by phone to determine the test-retest reliability of the RPQ when administered by phone. Results The RPQ demonstrated excellent test-retest reliability when delivered in person (ICC = 0.78, 95% Confidence Interval, CI: 0.59–0.89) and by phone (ICC = 0.82, 95% CI: 0.65–0.91), good internal consistency (α = 0.78) and adequate construct validity (r = 0.52, 95% CI: 0.20–0.74, p = 0.003) in older adults with DM2. Conclusion Given the good psychometric properties in this sample of persons with Diabetes, the RPQ has the potential to be used in clinical practice as a risk assessment and fall prevention tool. However, further testing needs to be done using a larger sample.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".