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Record W4245432245 · doi:10.2196/preprints.34537

Rehabilitation Supported by Technology: Protocol for an International Cocreation and User Experience Study (Preprint)

2021· preprint· en· W4245432245 on OpenAlexaff
Sylvie Bernaerts, Nele De Witte, Vicky Van der Auwera, Bert Bonroy, Luiza Muraru, Panagiotis D. Bamidis, Christos A. Frantzidιs, Chrysoula Kourtidou‐Papadeli, Nancy Azevedo, Jokin Garatea, Idoia Muñoz, Rosa Almeida, Raquel Losada, Joyce Fung, Eva Kehayia, Anouk Lamontagne, Élaine de Guise, Cyril Duclos, Johanne Higgins, Sylvie Nadeau, Lucie Beaudry, Evdokimos Konstantinidis

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsUniversité du Québec à MontréalMcGill UniversityUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsContext (archaeology)Protocol (science)PreprintScale (ratio)Psychological interventionComputer scienceProcess managementKnowledge managementBusinessMedicineNursingWorld Wide WebGeographyAlternative medicine

Abstract

fetched live from OpenAlex

<sec> <title>BACKGROUND</title> Living labs in the health and well-being domain have become increasingly common over the past decade but vary in available infrastructure, implemented study designs, and outcome measures. The Horizon 2020 Project &lt;i&gt;Virtual Health and Wellbeing Living Lab Infrastructure&lt;/i&gt; aims to harmonize living lab procedures and open living lab infrastructures to facilitate and promote research activities in the health and well-being domain in Europe and beyond. This protocol will describe the design of a joint research activity, focusing on the use of innovative technology for both rehabilitation interventions and data collection in a rehabilitation context. </sec> <sec> <title>OBJECTIVE</title> With this joint research activity, this study primarily aims to gain insight into each living lab’s infrastructure and procedures to harmonize health and well-being living lab procedures and infrastructures in Europe and beyond, particularly in the context of rehabilitation. Secondarily, this study aims to investigate the potential of innovative technologies for rehabilitation through living lab methodologies. </sec> <sec> <title>METHODS</title> This study has a mixed methods design comprising multiple phases. There are two main phases of data collection: cocreation (phase 1) and small-scale pilot studies (phase 2), which are preceded by a preliminary harmonization of procedures among the different international living labs. An intermediate phase further allows the implementation of minor adjustments to the intervention or protocol depending on the input that was obtained in the cocreation phase. A total of 6 small-scale pilot studies using innovative technologies for intervention or data collection will be performed across 4 countries. The target study sample comprises patients with stroke and older adults with mild cognitive impairment. The third and final phases involve Delphi procedures to reach a consensus on harmonized procedures and protocols. </sec> <sec> <title>RESULTS</title> Phase 1 data collection will begin in March 2022, and phase 2 data collection will begin in June 2022. Results will include the output of the cocreation sessions, small-scale pilot studies, and advice on harmonizing procedures and protocols for health and well-being living labs focusing on rehabilitation. </sec> <sec> <title>CONCLUSIONS</title> The knowledge gained by the execution of this research will lead to harmonized procedures and protocols in a rehabilitation context for health and well-being living labs in Europe and beyond. In addition to the harmonized procedures and protocols in rehabilitation, we will also be able to provide new insights for improving the implementation of innovative technologies in rehabilitation. </sec> <sec> <title>INTERNATIONAL REGISTERED REPORT</title> PRR1-10.2196/34537 </sec>

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.036
GPT teacher head0.342
Teacher spread0.306 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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