Defining the Role of Living Labs to Clinical Research: Initial Findings for Framework Development
Bibliographic record
Abstract
Integrating research into clinical environments is critical for advancing healthcare, yet various barriers hinder healthcare professionals from participating in externally initiated research efforts. This study investigates how Living Labs can support the integration of such research into routine clinical practice. Through a mixed-methods questionnaire distributed to Living Lab practitioners, we identify key barriers including time constraints, misalignment between research agendas and clinical needs, and inadequate incentive structures. The qualitative data about Living Lab offering were thematically analysed and mapped across four research phases, planning, preparation, implementation, and dissemination, as defined by the Living Lab Management System. Preliminary results, based on responses from seven Living Labs, suggest that Living Labs can enhance research relevance, foster co-ownership among professionals, and serve as hubs for multi-stakeholder engagement. They also provide mechanisms for academic recognition and professional development, which may act as incentives for clinician participation. The outcome of this work is an initial evidence-informed framework that outlines actionable strategies to address common barriers to clinical research implementation.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.360 | 0.349 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.012 | 0.032 |
| Scholarly communication | 0.023 | 0.027 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".