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Record W6962666938 · doi:10.17605/osf.io/q7zgh

Implementation characteristics and outcomes of virtual rehabilitation programs for individuals with spinal cord injury (SCI)

2024· other· en· W6962666938 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsTetraplegiaSpinal cord injuryRehabilitationParaplegiaQuality of life (healthcare)ParalysisAutonomic dysreflexia

Abstract

fetched live from OpenAlex

This is a scoping review that will explore the implementation characteristics and outcomes of the virtual rehabilitation programs for individuals with spinal cord injury (SCI). Spinal cord injury (SCI) is a debilitating condition which can be traumatic or nontraumatic in origin and can lead to complete or partial sensory and/or motor paralysis of the body below the level of injury (Nas et al., 2015). In Canada, the estimated prevalence of SCI is approximately 86000 including both traumatic and nontraumatic injury (Noonan et al., 2012), of which 44000 (51%) are traumatic and 42000 (49%) are nontraumatic in origin (Noonan et al., 2012). Of these individuals, 56% are living with paraplegia and 44% are living with tetraplegia (Noonan et al., 2012). The individuals living with SCI often develop many acute and long term secondary complications which include pressure sores/ulcers, urinary infections, bladder and bowel incontinence, autonomic dysreflexia, and respiratory infections which affects these individuals not only physically, but also mentally, socially, and economically and this can cause depression, loss of independence, and reduced quality of life in these individuals (Sezer et al., 2015, Pilusa et al., 2019, Lee et al., 2021, Thorogood et al., 2023). These secondary health conditions are the leading cause of re-hospitalization and increased mortality in individuals with SCI (Sezer et al., 2015, Pilusa et al., 2019). Therefore, it becomes important to prevent or reduce these secondary health conditions in these individuals and this is often overwhelming to the individuals and their caregivers as this process involves early diagnosis, treatment, and rehabilitation which requires multidisciplinary healthcare services (Lee et al., 2021, Touchett et al., 2022). Many individuals have difficulty accessing needed healthcare services due to remote locations and financial and time constraints (Touchett et al., 2022). Also, during the covid-19 pandemic, it became almost impossible for these individuals to access healthcare services as they are immunocompromised and often develop infections very easily and quickly, and the services were also restricted and disrupted during pandemic times (Swarnakar et al., 2023). During the pandemic, tele health or tele medicine emerged as a safe and promising alternative to deliver in-person healthcare services (Touchett et al., 2022, Swarnakar et al., 2023). Rehabilitation services using telephone or video conferencing (i.e., tele-rehabilitation) is helpful to overcome barriers to accessing SCI care and needs to be considered and explored further. Tele-rehabilitation allows individuals to access healthcare through devices (such as a computer or iPad) from convenient locations. Moreover, the global pandemic augmented the use of virtual rehabilitation (Touchett et al., 2022) which is reported to be safe, feasible, and effective in improving self-care and mobility in individuals with SCI (Coulter et al., 2017, Sweet et al., 2017, Swarnakar et al., 2023). A scoping review was done in 2023 to describe and compare the models of telehealth services for community-dwelling adults with SCI. However, there is no evidence available about the implementation characteristics and outcomes of virtual rehabilitation programs for individuals with SCI. Therefore, the objective of this scoping review is to explore the implementation characteristics and outcomes of virtual rehabilitation programs for individuals with SCI. Research question: What literature exists to describe the implementation characteristics and outcomes of the interventions used for virtual rehabilitation of individuals with SCI?

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.063
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0040.008
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.032
GPT teacher head0.418
Teacher spread0.387 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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