The association of multiple built environment factors with a clinical measure of grip strength
Bibliographic record
Abstract
Background: Population-level interventions that promote healthy aging through modifications to the built environment are likely to be more effective than individual-level interventions. Few studies have examined the influence of multiple built environment factors on measures of healthy aging. Objectives: We leveraged detailed data from a population-based cohort study to examine how multiple aspects of the built environment were associated with grip strength, a well-accepted measure of age-related health status. Methods: ), greenness, light-at-night, and walkability were linked to participant residential postal codes. Grip strength was measured using a digital hydraulic hand dynamometer. Logistic regression analyses were used to estimate the odds of having sex-specific clinically weak measures of grip strength in association with each built environment factor. The other built environment factors, demographics, and lifestyle factors were evaluated as confounders. Results: and greenness were statistically significantly associated with increased and decreased odds of having clinically weak grip strength, respectively, after adjusting for demographic, lifestyle, and other built environment factors. Conclusion: Our findings suggest that built environment factors are compelling targets for improving age-related health, though the mechanisms underlying associations with these factors, particularly greenness, remain uncertain.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".