Protocol for co-producing a framework and integrated resource platform for engaging patients in laboratory-based research
Bibliographic record
Abstract
BACKGROUND: Patient engagement in research is the meaningful and collaborative interaction between patients and researchers throughout the research process. Patient engagement can help to ensure patient-oriented values and perspectives are incorporated into the development, conduct, and dissemination of research. While patient engagement is increasingly prevalent in clinical research, it remains relatively unrealized in preclinical laboratory research. This may reflect the nature of preclinical research, in which routine interactions or engagement with patients may be less common. Our team of patient partners and researchers has previously identified few published examples of patient engagement in preclinical laboratory research, as well as a paucity of guidance on this topic. Here we propose the development of a process framework to facilitate patient engagement in preclinical laboratory research. METHODS: Our team, inclusive of researchers and patient partners, will develop a comprehensive, empirically-derived, and stakeholder-informed process framework for 'patient engagement in preclinical laboratory research.' First, our team will create a 'deliberative knowledge space' to conduct semi-structured discussions that will inform a draft framework for preclinical patient engagement. Over the course of several sessions, we will identify actions, activities, barriers, and enablers (e.g. considerations and motivations for patient engagement in preclinical laboratory research, define roles of key players). The resulting draft process framework will be further populated with examples and refined through an international consensus-building Delphi survey with patients, researchers, and other collaborator organizations. We will then conduct pilot field tests to evaluate the framework with preclinical laboratory research groups paired with patient partners. These results will be used to create a refined framework enriched with real-world examples and considerations. All resources developed will be made available through an online repository. DISCUSSION: Our proposed process framework will provide guidance, best practices, and standardized procedures to promote patient engagement in preclinical laboratory research. Supporting and facilitating patient engagement in this setting presents an exciting new opportunity to help realize the important impact that patients can make.
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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 | 0.224 | 0.223 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.096 | 0.034 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".