Investigating mobile applications for driving rehabilitation after stroke in occupational therapy: the patient, caregiver, and clinician perspective
Notice bibliographique
Résumé
With medical advancements, more Canadians are surviving a stroke. However, many live with residual impairments that can affect their everyday function. Regaining the ability to drive is often a priority among patients after stroke. Current evidence indicates there is a critical need for evidence-based interventions that support their return to this occupation. In the first study, OTs identified assessments and interventions they used to address driving post-stroke. From the breadth of interventions, the use of mobile applications was identified as a major and significant knowledge gap by clinicians, as to how their patients perceived and used this technology when deployed. Following this study, community-dwelling patients with stroke and their caregivers were provided with DriveFocus®; a new mobile application for driving rehabilitation. Their use of DriveFocus® was tracked for four weeks from which distinct patterns with using this technology emerged. Follow-up interviews with participants explored these patterns. Guided by a technology acceptance model, this mixed-methods analysis showed how the presence and absence of certain factors (e.g., having a ‘tech-savvy’ caregiver) can support technology adoption. Participants also described how OTs play a key role with introducing and monitoring their use of this technology during stroke rehabilitation. In the final study, clinicians from the first study as well as additional OTs were recruited. Their interviews identified factors that influenced how they selected and deployed mobile applications, like DriveFocus®, to address a patient’s goal of returning to driving. These factors included clinician awareness of emerging technology and mobile applications, workplace policies that support the upkeep and integration of technology as well as the patients’ level of impairment and comfort with using mobile technology. Having caregivers to facilitate uptake of this technology was also raised during these interviews. This thesis opened by exploring the process by which the occupation of driving is addressed by OTs in stroke rehabilitation where subsequent studies identified factors specific to the uptake of mobile application by individuals with stroke, their caregivers, as well as clinicians to address this occupation. In the closing chapter, these factors are described using an OT model that highlighted opportunities and challenges for implementing mobile technology for driving within stroke rehabilitation.
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 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 | 0,010 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».