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Examination of Winter Driving using In-vehicle Devices and the Perceptions of Older Drivers

2010· dissertation· en· W2604046025 sur OpenAlexaboutno aff
Aileen Trang

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2010
Typedissertation
Langueen
DomaineHealth Professions
ThématiqueOlder Adults Driving Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPerceptionPsychologyTransport engineeringApplied psychologyEngineering
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Introduction: Although several studies have examined self-regulatory practices in older drivers, most have relied on self-report. Blanchard (2008) was the first to examine actual driving patterns more objectively (using in-vehicle devices), and the associations between driver perceptions and self-regulatory practices. However, her sample of older drivers living in Southwestern Ontario was only monitored for one week between June and October. Winter conditions in northern climates appear to influence the driving patterns of older adults, however the only evidence to date is based on self-report (e.g., Sabback & Mann, 2005). 
\nPurposes: The aims of the thesis were to: 1) replicate Blanchard’s findings on the associations between driver perceptions and self-regulatory practices in older drivers; and 2) extend this investigation by examining driving over a longer monitoring period in the winter. Methods: A convenience sample of 47 drivers aged 65 to 91 (49% female) from Southwestern Ontario was monitored for two consecutive weeks between late November and March. Driving data was collected using two electronic devices (one with GPS), which were installed at the first of two home visits. Information on weather and road conditions was collected from archives and descriptions in participant trip logs. Participants completed questionnaires concerning background and usual driving habits. Driver perceptions were assessed using the Driving Comfort (DCS) and Perceived Driving Abilities (PDA) scales, while self-reported usual practices were examined using the Situational Driving Frequency (SDF) and Avoidance (SDA) scales. Functional driving-related abilities were assessed using the AAA/CAA’s Roadwise Review and interviews were conducted at the second home visit, at which point devices were removed and trip logs collected. 
\nResults: Driver perceptions (particularly night comfort) were significantly related to multiple indictors of driving (distance, duration, radius from home and night driving) in the expected directions. Men had higher comfort scores and better perceptions of their driving abilities and concurrently drove more often, greater distances and further from home. Participants drove on average five days a week over the winter monitoring period. Over half the 94-day monitoring period had inclement weather, while 67% of the period had poor road conditions. Nonetheless, all 46 participants drove at least once in bad weather and 73% did so in darkness. Distance driven at night varied by month of participation, with people driving more at night during December (average 50 km), compared to March (average of only 13 km). Those with lower daytime comfort scores (>50%) scores drove less on days with inclement weather (p=.03). The sample was also more likely to make social trips on clear days (p=.002) and out-of-town trips on days with good road conditions (p=.02). 
\nConclusions: The study replicated Blanchard’s (2008) findings that driver perceptions are strongly associated with actual behaviour, regardless of the season. And both studies indicate that older drivers may not self-regulate as much as they say they do on avoidance questionnaires. Driving was fairly consistent over the two weeks, except for radius and night distance and the additional week of monitoring was more likely to capture night driving. Nonetheless, the present study provides only a snapshot of behaviour and findings should not be generalized beyond urban dwelling, well-educated, healthy and active older drivers from one part of Canada. Further studies, with larger more diverse samples (living in different regions) and longer monitoring periods, are required to advance our knowledge of self-regulatory practices in older drivers and related decision-making processes.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,427
Score d'incertitude au seuil0,982

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,016
Tête enseignante GPT0,291
Écart entre enseignants0,275 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2010
Routes d'admission1
Résumé présentoui

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