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Record W4322582617 · doi:10.2196/44591

Clinicians’ Experiences of Implementing a Telerehabilitation Toolkit During the COVID-19 Pandemic: Qualitative Descriptive Study

2023· article· en· W4322582617 on OpenAlexafffundvenueabout
Sarah Munce, Angie Andreoli, Mark Bayley, Meiqi Guo, Elizabeth L. Inness, Ailene Kua, McKyla McIntyre

Bibliographic record

VenueJMIR Rehabilitation and Assistive Technologies · 2023
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoInstitute of Health Services and Policy ResearchUniversity Health Network
FundersToronto Rehabilitation Institute
KeywordsTelerehabilitationPandemicCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakMedical educationSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicinePsychologyTelemedicineComputer scienceVirologyHealth careDiseasePathologyPolitical scienceInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

BACKGROUND: Although the COVID-19 pandemic resulted in a rapid implementation and scale-up of telehealth for patients in need of rehabilitation, an overall slower scaling up to telerehabilitation has been documented. OBJECTIVE: The purpose of this study was to understand experiences of implementing telerehabilitation during the COVID-19 pandemic as well as using the Toronto Rehab Telerehab Toolkit from the perspective of rehabilitation professionals across Canada and internationally. METHODS: The study adopted a qualitative descriptive approach that consisted of telephone- or videoconference-supported interviews and focus groups. Participants included rehabilitation providers as well as health care leaders who had used the Toronto Rehab Telerehab Toolkit. Each participant took part in a semi-structured interview or focus group, lasting approximately 30-40 minutes. Thematic analysis was used to understand the barriers and enablers of providing telerehabilitation and implementing the Toronto Rehab Telerehab Toolkit. Three members of the research team independently analyzed a set of the same transcripts and met after each set to discuss their analysis. RESULTS: A total of 22 participants participated, and 7 interviews and 4 focus groups were included. The data of participants were collected from both Canadian (Alberta, New Brunswick, and Ontario) and international sites (Australia, Greece, and South Korea). A total of 11 sites were represented, 5 of which focused on neurological rehabilitation. Participants included health care providers (ie, physicians, occupational therapists, physical therapists, speech language pathologists, and social workers), managers and system leaders, as well as research and education professionals. Overall, 4 themes were identified including (1) implementation considerations for telerehabilitation, encompassing 2 subthemes of "infrastructure, equipment, and space" and "leadership and organizational support"; (2) innovations developed as a result of telerehabilitation; (3) the toolkit as a catalyst for implementing telerehabilitation; and (4) recommendations for improving the toolkit. CONCLUSIONS: Findings from this qualitative study confirm some of the previously identified experiences with implementing telerehabilitation, but from the perspective of Canadian and international rehabilitation providers and leaders. These findings include the importance of adequate infrastructure, equipment, and space; the key role of organizational or leadership support in adopting telerehabilitation; and availing resources to implement it. Importantly, participants in our study described the toolkit as an important resource to broker networking opportunities and highlighted the need to pivot to telerehabilitation, especially early in the pandemic. Findings from this study will be used to improve the next iteration of the toolkit (Toolkit 2.0) to promote safe, accessible, and effective telerehabilitation to those patients in need in the future.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.120
GPT teacher head0.469
Teacher spread0.349 · 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 teacher head, not a consensus.

Study designObservational
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

Citations25
Published2023
Admission routes4
Has abstractyes

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