Protocol for a systematic review of living labs in healthcare
Bibliographic record
Abstract
INTRODUCTION: Healthcare is increasingly challenged to meet the demands of user involvement and knowledge mobilisation required by the 21st-century patient-centred and knowledge-based economies. Innovations are needed to reduce problematic barriers to knowledge exchange and improve collaborative problem solving. Living labs, as open knowledge systems, have the potential to address these gaps but are underexplored in healthcare. METHODS AND ANALYSIS: We will conduct the first systematic review of living labs across healthcare contexts. We will comprehensively search the following online databases from inception to 31 December 2020: Scopus, the Cochrane Library (Wiley), Medline (OVID), Embase (OVID), Web of Science, PsycINFO (OVID) and EBSCOhost databases including Academic Search Complete, Business Source Premier, Canadian Reference Centre, CINAHL, MasterFILE Premier, SPORTDiscus, Library & Information Science Source, Library, Information Science & Technology Abstracts, AgeLine, EconLit, Art Full Text, Women's Studies International and Social Work Abstracts. We will search for grey literature using Google advanced techniques and books/book chapters through scholarly and bibliographical databases. We will use a dual-reviewer, two-step selection process with pre-established inclusion criteria and limit to English language publications. Empirical studies of any design examining living lab development, implementation or evaluation in health or healthcare will be included. We will use the Mixed Methods Appraisal Tool (MMAT) for methodological quality appraisal and Covidence software for review management, and we will extract data on pre-established variables such as lab context and technological platforms. We will create evidence tables and analyse across variables such as focal aim and achievement of living lab principles, such as the use of cocreation and multimethod approaches. We will tabulate data for descriptive reporting and narrative synthesis to identify current applications, approaches and promising areas for living lab development across health contexts. ETHICS AND DISSEMINATION: Ethical approval was not required for this review. This review will inform research into living labs in health environments, including guidance for a living lab in paediatric rehabilitation. Academic publications shared through collaborative networks and social media channels will provide substantive knowledge to the growing tech-health development sector and to researchers, practitioners and organisations seeking enhanced patient/stakeholder engagement and innovations in knowledge translation and evidence-based practice. PROSPERO REGISTRATION NUMBER: CRD42020175275.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".