Additive and interactive effects of social, financial and environmental factors on gait speed among community dwelling older adults in Nigeria: A cross-sectional study
Bibliographic record
Abstract
Gait speed is considered the sixth vital sign in geriatric care, due to its predictive role in health and social outcomes. Although various factors influence gait speed, environmental and social factors are frequently overlooked, particularly in developed regions with distinct cultural perspectives. The study examines how these environmental, financial and social factors additively or interactively influence gait speed of community dwelling older adults in Nigeria. We employed a cross-sectional study design included 408 community-dwelling older adults (mean [S.D] = 68.0(6.6) years) from a city in Nigeria. We administer validated measures to assess nine neighborhood environmental factors, five social factors and self-reported income. Gait Speed was assessed using the 10-metre walk test and categorized < 0.8m/s (slow speed) and ≥0.8m/s (normal). Logistic regression analysis was used to determine the predictors of gait speed in this dichotomized form. Slow gait speed prevalence was 72.4%. The additive model revealed that age (OR = 0.798, p < 0.001), social network (OR = 1.079, p < 0.01), and neighborhood surroundings (OR = 1.117, p < 0.01) were significant predictors of high gait speed. In the interaction model, age, social network, and neighborhood surroundings remained significant predictors and education level became significant: secondary education (OR = 3.986, p = 0.047) and tertiary education (OR = 4.580, p = 0.038) associated with higher odds of high gait speed. Findings suggest that enhancing social networks and improving neighborhood environments may be crucial in promoting better mobility outcomes, particularly among diverse older adults.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".