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Record W4242572457 · doi:10.32920/ryerson.14655957

Walking in the winter: a qualitative study to identify environmental barriers encountered by seniors

2021· preprint· en· W4242572457 on OpenAlexaffabout
Herthana Siva

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsToronto Metropolitan UniversityUniversity of Waterloo
Fundersnot available
KeywordsWalkabilityPhotovoiceBuilt environmentInterimAuditLevel designPromotion (chess)Occupational safety and healthPsychologyTransport engineeringEnvironmental healthGeographyBusinessMedicineEngineeringPolitical scienceCivil engineeringComputer science

Abstract

fetched live from OpenAlex

The purpose of this research was to explore features in the built environment that are considered to be barriers by seniors when walking in the winter. Nine seniors across four neighbourhoods in the City of Mississauga participated in the study. A combination of photovoice and semi-structured interviews was used to collect data. Findings revealed safety related to fall hazards and traffic conditions as major concerns among the seniors. Participants provided suggestions for potential changes to improve walking conditions for seniors, including: judicious placement of interim crosswalks, more open public washrooms, additional benches in parks, and railings along sloping sidewalks and cameras in parks. This research contributes to the limited literature that investigates the role of the built environment on physical activity levels among seniors. Results can help inform questions for the development of a walkability audit tool, public health promotion strategies and municipal land use policies to build healthy communities.

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)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.004
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.426
Teacher spread0.392 · 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 designQualitative
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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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