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Record W7106291392 · doi:10.1080/29941849.2025.2590908

“I travel because I want to be well”: How older Canadians perceive daily travel as contributing to their quality of life and well-being

2025· article· en· W7106291392 on OpenAlexafffundabout

Bibliographic record

VenueSustainable Transport and Livability · 2025
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsMcGill University
FundersNational Research Council CanadaMcGill University
KeywordsQuality of life (healthcare)Quality (philosophy)PerceptionTourism

Abstract

fetched live from OpenAlex

Independent mobility and access to desired opportunities are essential determinants of older adults’ well-being. However, as they experience life transitions and changes to their mobility, older adults’ conceptualization of how travel impacts their own quality of life and well-being can evolve. Moreover, although quality of life and well-being can be considered separately in research, the two concepts may be intertwined in the lived travel experiences of older adults. Based on the results of a Canadian multi-city survey, this study investigates older adults’ (65+) evaluation of the contribution of their daily travel on their quality of life and well-being using a mixed-methods approach. An ordered probit model (n = 2342) examines the agreement with the following statement: “My daily travel contributes positively to my quality of life”, revealing higher agreement among older adults who use public transit frequently, who wish to keep travelling independently, live in walkable areas and are satisfied with their lives and physical health. To add nuance to these results, two waves of in-depth interviews were conducted. Findings from a large of interviews (n = 56) outline influential factors such as the significance of walking for maintaining mental and physical health, supporting overall well-being. Follow-up interviews (n = 35) reveal that older adults generally see quality of life and well-being as synonymous, particularly when considering how daily travel affects their lives. This research can be of value to transport professionals working towards improving older adults’ quality of life and well-being.

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0080.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.018
GPT teacher head0.331
Teacher spread0.313 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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