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Record W3159243089 · doi:10.1136/bmjopen-2020-041530

Living labs for patient engagement and knowledge exchange: an exploratory sequential mixed methods study to develop a living lab in paediatric rehabilitation

2021· article· en· W3159243089 on OpenAlexafffund
Mandy M. Archibald, Kristy Wittmeier, Matthew Ryan Gale, M. Florencia Ricci, Kelly Russell, Roberta L. Woodgate

Bibliographic record

VenueBMJ Open · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsThematic analysisKnowledge translationMedicineMedical educationData collectionRehabilitationQualitative researchHealth careQualitative propertyIndependent livingNursingKnowledge managementGerontologyComputer science

Abstract

fetched live from OpenAlex

INTRODUCTION: Despite recognition of the importance of patient engagement in research and knowledge translation, systematic approaches to engagement and co-ideation remain limited. Living labs are collaborative knowledge sharing systems that use multimethod, user-centred approaches that hold potential to catalyse these aims. However, their use in healthcare is limited, and no living lab has been developed in paediatric rehabilitation. In response to this gap and to propel innovative knowledge exchange, we propose a mixed methods study to co-develop a living lab prototype (ie, preliminary infrastructure with opportunity for scale up) in paediatric rehabilitation, with relevance to other healthcare contexts. METHODS: An exploratory sequential mixed methods study will be undertaken to determine research and knowledge exchange priorities and to inform the development of the living lab prototype. Stage 1: we will use a multipronged approach to sample 18-21 youth with developmental differences or rehabilitation needs, their youth siblings and parents/guardians from a provincial paediatric rehabilitation centre, to participate in qualitative and arts-based data collection. Data will provide insight into desirable features of the living lab. Stage 2: E-surveys to youth, siblings, parents/guardians and clinicians who receive or provide services at this same centre will expand on priorities and living lab features. Stage 3: integrated analysis will inform the living lab prototype development. ANALYSIS: Inductive thematic analysis using interpretive description, integrated analysis of visual data and descriptive and content analysis of e-survey data will be undertaken. Joint displays will facilitate data integration. Priorities will be identified using a modified rank-order method for each key living lab domain. ETHICS AND DISSEMINATION: Institutional ethics and site approval have been granted. A parent advisory group and rehabilitation engineering partners will confer on data and inform the development of the living lab prototype. User engagement with the prototype will occur during an online or in-person event, and findings shared through non-technical research summaries, journal articles and academic presentations.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaOpen science
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
gptMetaresearch
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.051
metaresearch head score (Gemma)0.041
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 score0.051
Threshold uncertainty score0.269

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0050.003
Scholarly communication0.0040.005
Open science0.0030.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.119
GPT teacher head0.410
Teacher spread0.291 · 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

Labeled directly by 2 models reading the full record.

Open scienceMetaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designQualitative
DomainMethods
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

Citations16
Published2021
Admission routes2
Has abstractyes

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