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Record W4386862323 · doi:10.2196/52489

Built Environments to Support Rehabilitation for People With Stroke From the Hospital to the Home (B-Sure): Protocol for a Mixed Method Participatory Co-Design Study

2023· article· en· W4386862323 on OpenAlexvenueno aff
Maya Kylén, Jodi Sturge, Ruby Lipson‐Smith, Steven Schmidt, Hélène Pessah‐Rasmussen, Tony Svensson, Laila de Vries, Julie Bernhardt, Marie Elf

Bibliographic record

VenueJMIR Research Protocols · 2023
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsnot available
FundersSvenska Forskningsrådet Formas
KeywordsRehabilitationProtocol (science)StakeholderProcess managementProcess (computing)Qualitative researchDescriptive statisticsData collectionComputer scienceApplied psychologyNursingPsychologyMedicineEngineeringSociologyPhysical therapyPublic relations

Abstract

fetched live from OpenAlex

BACKGROUND: A global trend is to move rehabilitation closer to people's neighborhoods and homes. Still, little attention has been given to how the built environment outside the hospital setting might impact rehabilitation and recovery for stroke survivors. OBJECTIVE: The overarching objective of this project is to develop conceptual models of built environments that support stroke rehabilitation and recovery outside the hospital setting. Specifically, the project will explore factors and characteristics of the built environment that support people with stroke and their families and identify innovative built environments that can be designed for local health care. The project will examine facilitators and obstacles for implementing built environmental solutions and evaluate the potential benefits, feasibility, and acceptability. METHODS: The project uses a mixed methods design approach with 3 phases. In phase 1, factors and characteristics of the built environment for rehabilitation will be identified. Based on the results from phase 1, phase 2 will involve co-designing prototypes of environments to support the rehabilitation process for people with stroke. Finally, the prototypes will be evaluated in phase 3. Qualitative and quantitative methods will include a literature review, a concept mapping (CM) study, stakeholder interviews, prototype development, and testing. The project will use multidimensional scaling, hierarchical cluster analysis, descriptive statistics for quantitative data, and content analysis for qualitative data. Location analysis will rely on the location-allocation model for network problems, and the rule-based analysis will be based on geographic information systems data. RESULTS: As of the submission of this protocol, ethical approval for the CM study and the interview study has been obtained. Data collection is planned to start in September 2023 and the workshops later in the same year. The scoping review is ongoing from January 2023. The CM study is ongoing and will be finalized in the spring of 2024. We expect to finish the data analysis in the second half of 2024. The project is a 3-year project and will continue until December 2025. CONCLUSIONS: We aim to determine how new environments could better support a person's control over their day, environment, goals, and ultimately control over their recovery and rehabilitation activities. This "taking charge" approach would have the greatest chance of transferring the care closer to the patient's home. By co-designing with multiple stakeholders, we aim to create solutions with the potential for rapid implementation. The project's outcomes may target other people with frail health after a hospital stay or older persons in Sweden and anywhere else. The impact and social benefits include collaboration between important stakeholders to explore how new environments can support the transition to local health care, co-design, and test of new conceptual models of environments that can promote health and well-being for people post stroke. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52489.

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)0.019
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.104
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.534
GPT teacher head0.670
Teacher spread0.136 · 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 designNot applicable
Domainnot available
GenreProtocol

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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