Association between accessibility to key activities through multi-modal public transit network in the Montréal Metropolitan Region and subjective well-being
Notice bibliographique
Résumé
Background: Access to physical locations and social networks determines how we create relationships with each other and how we can access material goods or intangible resources like knowledge and activities through which we can better our lives. Public transit may in that context influence individual subjective well-being through both the transit experience (during the trip) and as a mean of accessing key locations to meet our basic needs and promote personal development, increasing one’s satisfaction with their life.Goal: Using data from the 1st wave of Montréal participants from INTERACT, collected in 2018, this cross-sectional cohort design aims to determine if public transit accessibility is associated with increased subjective wellbeing scores at the individual level.Methods: 833 participants completed the VERITAS questionnaire (map-based survey) for the first cycle of the INTERACT study in 2018. The main exposure, the fit between transportation needs and public transit offer at the individual level (transit fit measure), was a new metric developed using open-access data from Google Maps. The outcome of interest, life satisfaction (a component of subjective well-being), was measured using the Personal Well-being Index 5th Edition (PWBI score). Multiple linear regressions were performed to characterize the association between the main exposure and subjective well-being, controlling for the frequency of public transit use and other covariates that could have an influence on public transit use and well-being (car ownership, age, gender, education level and physical health).Results: A multiple linear regression model adjusting for yearly transit use, car ownership, age, gender, education, and physical health showed an average increase of 0.99 points in the PWBI score per increment of 1 in the transit fit measure (β = 0.99 with 95% CI [-0.40, 2.38]), which was not statistically significant. Age (β = 0.15 with 95% CI [0.08, 0.22]), reported physical health (β = 0.50 with 95% CI [0.37, 0.63]) and education level points (β = 3.97 with 95% CI [1.37, 5.58]) were associated with life satisfaction. There is a strong signal that high public transit use ≥ 5 times/week) is negatively associated with life satisfaction (β = -3.46 with 95% CI [-7.19, 0.27]). The model using all covariates had an adjusted R2 of 0.1075, meaning that this model only explained 10.75% of the variance of the outcome variable.Conclusions: A novel public transit accessibility measurement methodology was developed based on fit between a user’s lifestyle choices (through daily-activities-related trips) and public transit offer, which will need further refinement. Further research should be done to enhance our understanding of the mechanisms underlying the complex relationship between access to public transport and subjective well-being. Public transit can have an impact on individuals’ subjective well-being through multiple pathways, which highlights the need for an integrated, intersectoral strategy to increase access
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».