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Record W4379056445 · doi:10.2196/44498

Perspectives of Rehabilitation Professionals on Implementing a Validated Home Telerehabilitation Intervention for Older Adults in Geriatric Rehabilitation: Multisite Focus Group Study

2023· article· en· W4379056445 on OpenAlexafffundvenueabout
Margriet Pol, Amarzish Qadeer, Margo van Hartingsveldt, Mohamed-Amine Choukou

Bibliographic record

VenueJMIR Rehabilitation and Assistive Technologies · 2023
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of Manitoba
FundersMitacsNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsTelerehabilitationRehabilitationCoachingGeriatric rehabilitationIntervention (counseling)Focus groupTelehealthTelemedicineMedicineHealth carePhysical therapyPhysical medicine and rehabilitationNursingPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Owing to demographic trends and increasing health care costs, quick discharge with geriatric rehabilitation at home is advised and recommended for older adults. Telerehabilitation has been identified as a promising tool to support rehabilitation at home. However, there is insufficient knowledge about how to implement a validated home telerehabilitation system in other contexts. One of the major challenges for rehabilitation professionals is transitioning to a blended work process in which human coaching is supplemented via digital care. OBJECTIVE: The study aimed to gain an in-depth understanding of the factors that influence the implementation of an evidence-based sensor monitoring intervention (SMI) for older adults by analyzing the perspectives of rehabilitation professionals working in 2 different health ecosystems and mapping SMI barriers and facilitators. METHODS: We adopted a qualitative study design to conduct 2 focus groups, 1 in person in the Netherlands during winter of 2017 and 1 on the web via Zoom (Zoom Video Communications; owing to the COVID-19 pandemic) in Canada during winter of 2022, to explore rehabilitation providers' perspectives about implementing SMI. Qualitative data obtained were analyzed using thematic analysis. Participants were a group of rehabilitation professionals in the Netherlands who have previously worked with the SMI and a group of rehabilitation professionals in the province of Manitoba (Canada) who have not previously worked with the SMI but who were introduced to the intervention through a 30-minute web-based presentation before the focus group. RESULTS: The participants expressed different characteristics of the telerehabilitation intervention that contributed to making the intervention successful for at-home rehabilitation: focus on future participation goals, technology support provides the rehabilitation professionals with objective and additional insight into the daily functioning of the older adults at home, SMI can be used as a goal-setting tool, and SMI deepens their contact with older adults. The analysis showed facilitators of and barriers to the implementation of the telerehabilitation intervention. These included personal or client-related, therapist-related, and technology-related aspects. CONCLUSIONS: Rehabilitation professionals believed that telerehabilitation could be suitable for monitoring and supporting older adults' rehabilitation at home. To better guide the implementation of telerehabilitation in the daily practice of rehabilitation professionals, the following steps are needed: ensuring that technology is feasible for communities with limited digital health literacy and cognitive impairments, developing instruction tools and guidelines, and training and coaching of rehabilitation professionals.

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)0.022
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0080.003
Scholarly communication0.0030.003
Open science0.0010.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.383
Teacher spread0.365 · 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 designQualitative
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

Citations15
Published2023
Admission routes4
Has abstractyes

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