Predictive value of self-prioritized mobility factors on gait speed and life space in older nigerians: A cross-sectional study
Bibliographic record
Abstract
• Nigerian older adults say doctors should check their age, muscle power, strength, endurance and pain when discharging them. • Environmental, social, psychological, financial, physical, and personal factors predicted life space, but cognitive factors did not. • Environmental, social, psychological, cognitive, physical, and personal factors predicted gait speed, but financial factors did not. • Self-prioritized mobility factors explained 74 % of the variance in gait speed, stronger than the 50 % for life space. Eighty-two cognitive, environmental, financial, personal, physical, psychological, and social factors significantly influence mobility decline following hospital discharge. However, assessing all these factors during the fast-paced discharge process is impractical. This study aimed to identify the factors that Nigerian older adults consider most critical and determine which factors (in combination) most realistically predict gait speed and life space among these Nigerian older adults. This is data from a cross-sectional survey that recruited 400 Nigerian older adults, 60+ years old, to rank 82 factors influencing mobility. Older adults' gait speed and life-space mobility were collected using the 10-meter Walk Test and Life Space Assessment. Multivariate binary logistic regression was used to determine the most realistic predictor of gait speed and life-space mobility. No factors were considered critical by the older adults. The life space model indicates that increased street characteristics, social cohesion, occupation, hearing, gait speed, fear of falling, and conscientiousness accounts for approximately 50% of variations in life space. The gait speed model indicates that an increase in executive function, pain, respiratory system, body composition, fatigue, social factors, racial characteristics, marital status, social network, and fear of reinjury explain about 74 % of variation in gait speed. This study provides self-reported factors that could influence older adults' mobility following discharge that would allow clinicians to prioritize factors for assessment amidst multiple factors.
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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 | 0.001 | 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 | 0.001 |
| 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".