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Record W7161996932 · doi:10.82308/29714

Healthy aging in the neighborhood: Examining the relationship between the micro-scale built environment and walking in older adults

2021· dissertation· en· W7161996932 on OpenAlexaboutno aff
Madeleine Steinmetz-Wood

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsBuilt environmentReliability (semiconductor)Field (mathematics)PopulationLevel designHealthy agingAudit

Abstract

fetched live from OpenAlex

Evidence suggests that neighborhood-built environments influence walking behavior in older adults. Most studies to date have examined how macro-scale features (connectivity, land-use mix, and population density) encourage walking in this population. Findings about neighborhood macro-scale features and walking, however, are not often practical to apply in existing neighborhood settings, as changing these features can require substantial reconfiguration of the neighborhood layout. Altering micro-scale features of neighborhoods (e.g., presence and quality of sidewalks, benches) may be a relatively cost-effective and efficient method of creating environments that are conducive to walking. This dissertation adopted an explanatory mixed methods approach to better understand the relationship between the micro-scale environment and walking. The main findings of this dissertation are: 1. Reporting a research design and an integration strategy in mixed methods studies in the built environment and health field could help to strengthen our ability to gain new insights into the multidimensional nature of the relationship between the built environment and health.2. Virtual-STEPS is a reliable tool for virtually assessing the micro-scale environment of neighborhoods. Percentage agreement between virtual and field audits, and for inter-rater agreement was 80% or more for most items. There was high reliability between virtual and field audits with Kappa and ICC statistics indicating that 50.0% of items had almost perfect agreement and 32.5% of items had substantial agreement. Inter-rater reliability was also high with 42.5% of items with almost perfect agreement and 27.5% of items with substantial agreement.3. The micro-scale environment collectively promoted leisure walking in adults. The grand micro-scale score was associated with elevated odds of walking for leisure for at least 150 minutes per week in adults from Montreal and Toronto, even after accounting for self-selection. Conversely, the association between micro-scale walkability and walking for utilitarian purposes was inconclusive. 4. The micro-scale environment promoted leisure walking in older adults. The grand micro-scale score was associated with greater odds of walking for leisure for at least 150 minutes per week. After stratifying for health conditions, the grand micro-scale score and the traffic calming total section score were only associated with walking for leisure in the sample with health conditions, further the aesthetics section score became significantly associated with walking for leisure in the sample of older adults with health conditions. 5. Semi-structured interviews conducted with older adults living in the suburbs of Montreal during the COVID-19 pandemic revealed that aesthetics, pedestrian infrastructure, proximity to shops/facilities, and building characteristics were perceived as walk-friendly, whereas traffic as well as unsafe intersections were perceived as barriers to walking. Older adults also reported avoiding crowded parks and crowded or narrow boardwalks, sidewalks, and walking paths due to difficulties with physical distancing. Interventions to improve the micro-scale environment of neighborhoods could increase walking for leisure in older adults, a vulnerable population group, that may be particularly sensitive to the micro-scale features of their neighborhood environment

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.038
GPT teacher head0.316
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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