An exploration of fall-related, psychosocial variables in people with multiple sclerosis who have fallen
Bibliographic record
Abstract
Introduction Psychosocial contributors to fall risk for people with multiple sclerosis are often overlooked in falls prevention practice. This study explored several fall-related, psychosocial variables and their association with falls self-efficacy in a sample of people with multiple sclerosis reporting a fall. Method A cross-sectional, structured telephone survey was employed. The survey explored socio-demographics, multiple sclerosis characteristics, and fall-related psychosocial variables. Multiple linear regression was employed to investigate associations with Falls Efficacy Scale – International scores. Results The mean Falls Efficacy Scale – International score for 140 participants was 38.14(SD = 10.16), and the mean Falls Control Scale score was 5.38(SD = 2.22). Fear of falling was expressed by 129 (92%) participants, with 111 (79%) reporting associated activity curtailment. A regression model including six predictors explained 47% of the variance in the Falls Efficacy Scale – International scores. Results of the multiple linear regression showed that fear of falling, associated activity curtailment, balance interference, falls control, and health status were associated with falls self-efficacy. Conclusion Fear of falling and associated activity curtailment, low falls self-efficacy, and compromised falls control are common among people with multiple sclerosis who have fallen. These fall-related psychosocial variables are distinct and each warrants attention during assessment. Findings suggest that falls self-efficacy among people with multiple sclerosis who have fallen is a complex construct associated with physical and psychosocial factors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".