Finding Factors in Footfalls: Exploring the Factor Structure of Gait in Older Adults.
Bibliographic record
Abstract
Abstract Gait is a reputed marker of global health spanning various bodily systems (MacDonald et al., 2017) and is a robust predictor of deleterious age-related outcomes (Van Kan et al., 2009). However, the sheer number of individual gait variables employed as predictors in the existing literature can obscure interpretations. To address this issue, researchers have explored the factor structure of gait indicators to explain variance in age-related gait performance, identifying disparate models characterized by three to five underlying latent gait constructs comprised of 8 to 23 indicators (Hollman et al., 2011; Lord et al., 2013). Beyond this heterogeneity, additional limitations characterizing this literature include solutions that assume statistical independence among gait constructs, as well as inclusion of severely multicollinear indicators. Using data from the Healthy Minds Healthy Bodies (HMHB) study, the present research focused upon replicating and contrasting previous factor analytic efforts. HMHB participants (n=128) were healthy community-dwelling adults (Mage=72.81±5.24 years; female=100). Gait indicators from a GAITRite computerized walkway were selected according to a priori theoretical rationale, compatibility with previous studies, and consideration of multicollinearity. Gait factor structure was initially analyzed using principal component analysis. Results indicate the presence of three latent gait domains reflecting pace, rhythm, and variability, accounting for over 82.4% of the variance in gait performance. Current proceedings involve implementing confirmatory factor analysis to compare competing gait models. Findings will address disparities across factor models in the gait literature, as well as discuss the optimal number of factors for describing the underlying dimensionality of gait.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".