Translation, reliability, and validation of the Dutch Safe Use of Mobility Aid Checklist (SUMAC-NL) for walker use in people living with dementia
Bibliographic record
Abstract
<ns3:p>Background People with dementia have a yearly risk of falling of 60 to 80 percent. Therefore, a walker is often recommended. However, the use of a walker in people with dementia is associated with a threefold increased odds of falls compared to their healthy peers who walk with a walker. Better advice and training could reduce fall risk. Therefore, a tool to assess functional walking skills with a walker is needed. The SUMAC was developed to fill this gap. So far, there is no Dutch instrument available that can assess functional walking skills with a walker in people with dementia. Methods Reliability was evaluated by scoring videos of people with dementia (n = 9) using a walker performing the SUMAC-NL. ICC was used to assess inter-rater and test-retest reliability. An expert panel (n = 8) evaluated the content validity using the content validity index (CVI) and the content validity ratio (CVR). Results Inter-rater reliability of the SUMAC-NL was statistically significant for the physical function (PF) domain (ICC = 0.94, 95%CI (0.84, 0.98, p < 0.001) and for the interaction with equipment (EQ) domain (ICC = 0.79, 95%CI (0.49 – 0.95), p < 0.001). Test-retest reliability was statistically significant for both the PF domain (ICC = 0.95, 95%CI (0.89, 0.99), p < 0.001) and EQ domain (ICC = 0.92, 95%CI (0.82, 0.98), p < 0.001). The SUMAC-NL shows content validity with a CVI >0.79 for both domains and a CVR of 0.53 on the PF domain and 0.78 on the EQ domain. Conclusions The SUMAC-NL shows good to excellent reliability and content validity for both the PF and the EQ domain. The SUMAC-NL seems to be a promising tool to assess walking with a walker in people with dementia in the Netherlands.</ns3:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".