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Record W4380081270 · doi:10.2196/46408

Supporting Collaboration in Rehabilitation Trajectories With Information and Communication Technologies: Scoping Review

2023· article· en· W4380081270 on OpenAlexvenueno aff
Jo Inge Gåsvær, Randi Jepsen, Ilona Heldal, Tobba Therkildsen Sudmann

Bibliographic record

VenueJMIR Rehabilitation and Assistive Technologies · 2023
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
FundersHøgskulen på Vestlandet
KeywordsRehabilitationComputer scienceKnowledge managementPhysical medicine and rehabilitationHuman–computer interactionMedicinePhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Despite a surge in health information and communication technology (ICT), there is little evidence of lowered cost or increased quality of care. ICT may support patients, health care providers, and other stakeholders through complex rehabilitation trajectories by offering digital platforms for collaboration, shared decision-making, and safe storage of data. Yet, the questions on how ICT can become a useful tool and how the complex intersection between producers and users of ICT should be solved are challenging. OBJECTIVE: This study aims to review the literature on how ICTs are used to foster collaboration among the patient, the provider, and other stakeholders. METHODS: This scoping review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Studies were identified by searching MEDLINE (OVID), Embase (OVID), CINAHL (EBSCOhost), AMED (EBSCOhost), and Scopus. Unpublished studies were extracted from OAIster, Bielefeld Academic Search Engine, ProQuest Dissertations and Theses, NARIC, and Google Scholar. Eligible papers addressed or described a remote dialogue between stakeholders using ICT to address goals and means, provide decision support, or evaluate certain treatment modalities within a rehabilitation context. Due to the rapid development of ICTs, searches included studies published in the period of 2018-2022. RESULTS: In total, 3206 papers (excluding duplicates) were screened. Three papers met all inclusion criteria. The papers varied in design, key findings, and key challenges. These 3 studies reported outcomes such as improvements in activity performance, participation, frequency of leaving the house, improved self-efficacy, change in patients' perspective on possibilities, and change in professionals' understanding of patients' priorities. However, a misfit between the participants' needs and the technology offered, complexity and lack of availability of the technology, difficulties with implementation and uptake, and lack of flexibility in setup and maintenance reduced the value of ICT for those involved in the studies. The low number of included papers is probably due to the complexity of remote collaboration with ICT. CONCLUSIONS: ICT has the potential to facilitate communication among stakeholders in the complex and collaborative context of rehabilitation trajectories. This scoping review indicates that there is a paucity of research considering remote ICT-supported collaboration in health care and rehabilitation trajectories. Furthermore, current ICT builds on eHealth literacy, which may differ among stakeholders, and the lack of sufficient eHealth literacy and ICT knowledge creates barriers for access to health care and rehabilitation. Lastly, the aim and results of this review are probably most relevant in high-income countries.

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)0.032
metaresearch head score (Gemma)0.143
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.032
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.143
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0090.011
Bibliometrics0.0280.027
Science and technology studies0.0020.002
Scholarly communication0.0090.008
Open science0.0040.005
Research integrity0.0060.003
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.013
GPT teacher head0.342
Teacher spread0.329 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations5
Published2023
Admission routes1
Has abstractyes

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