596Two approaches for estimating propensity score weights for examining neighbourhood built environment and walking changes
Bibliographic record
Abstract
Abstract Background Walking is associated with the built environment, however, this association may be biased by residential self-selection. This study examined how walking duration changed with residential relocation, while accounting for unbalanced covariates that may contribute to residential self-selection, using two different propensity score inverse probability weight (IPW) methods. Methods Urban participants (n = 703) of Alberta’s Tomorrow Project with pre- and post-relocation neighbourhood built environment and walking data were included. A walkability index was created by aggregating estimates for population density, street connectivity, and destination diversity. Participants were categorized into three groups based on change in residential walkability (decreased, minimal change, or increased). The association between changes in walkability and walking duration (min/week) was modelled with linear regression. Two types of IPWs were applied: 1) manually generated from multinomial regression models, and 2) generated from generalized boosted models. Results All three groups increased walking duration from pre- to post-relocation, however the largest increase was among participants who had increased walkability (M = 73.2, SD = 388), followed by those with minimal change (M = 60.0, SD = 382) and decreased (M = 50.2, SD = 374) walkability. Longitudinal associations between walkability change and walking were not statistically significant (p < 0.05) in models with or without IPWs. Conclusions Changes in neighbourhood walkability were not associated with changes in walking, regardless of how the sample was weighted. Further research should examine changes in the neighbourhood environment with different types of walking and physical activity behaviours. Key messages IPW methods can be used to account for unbalanced covariates in analyses that involve possible self-selection bias.
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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 | 0.042 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".