Describing and Predicting the Possession of Assistive Devices Among Persons With Multiple Sclerosis
Bibliographic record
Abstract
OBJECTIVE: This study describes the types of assistive devices in the possession of persons with multiple sclerosis (MS) and identifies factors that best predicted the probability of possessing these devices. METHOD: A secondary analysis using frequency distributions and logistic regression of existing cross-sectional data was completed. Data were from an anonymous mail survey of members of the Multiple Sclerosis Society of Canada (Atlantic Division) (N = 906). RESULTS: Mobility aids and grab bars were the most commonly reported assistive devices. Seeing an occupational therapist, not working, having a progressive type of MS, having more activity limitations and more symptoms, and having MS for a longer period were found to increase the probability of possessing assistive devices. CONCLUSION: The descriptive results of this study are similar to studies of assistive technology use by older adults and persons with other chronic conditions. Type of MS and seeing an occupational therapist were the two strongest predictors of possessing assistive devices among respondents.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".