Investigating mobile applications for driving rehabilitation after stroke in occupational therapy: the patient, caregiver, and clinician perspective
Bibliographic record
Abstract
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.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".