Identifying Challenges and Enablers to Engaging Patients in Preclinical Laboratory Research
Bibliographic record
Abstract
Abstract Background Patient engagement in research enriches study design, conduct, and dissemination by integrating lived experiences of patients into the research process. Although patient engagement is increasingly common in clinical research settings, it remains rare in preclinical (i.e. laboratory based) research. To explore this gap, we conducted an interview study to understand how early adopters have implemented patient engagement in this area, focusing on the challenges and benefits of their approach. Methods We conducted semi-structured interviews of patients (n=15) and researchers (n=14) with previous preclinical patient engagement experience. Interviews were transcribed and conducted using an inductive, thematic content analysis, which allowed for bottom-up analysis of interview data. Our team inclusive of patients, clinical, preclinical and patient engagement researchers identified, reviewed, and refined emerging themes. Results We identified five themes. Researchers and patients highlighted the necessity to adopt a thoughtful and tailored approach for each preclinical engagement initiative (Theme 1). Clear communication was deemed critical, suggesting the need for a clear and shared vocabulary without technical jargon (Theme 2). This includes taking time to cultivate personal relationships, co-develop engagement activities to meet patient and researcher preferences and needs. In addition, v aried goals for engagement in preclinical research between researchers and patients was underscored (Theme 3), indicating the need to discuss aims and motivations early and often as well as to co-develop mutually beneficial strategies. Researchers and patients also discussed how they require a better understanding of the value of preclinical patient engagement (Theme 4). This could be fostered through education and illustrative case examples. Finally, a shift in research culture was deemed necessary (Theme 5), and called for stronger institutional support, efficient channels to connect preclinical researchers and patients, as well as initiatives that recognize and champion preclinical patient engagement. Conclusion Our study identified five common themes in preclinical patient engagement which can help the research community facilitate meaningful engagement of patients in preclinical laboratory research. Funding This work was supported by a Canadian Stem Cell Network Ethical, Legal and Social Implications Operating Grant. Patient engagement was supported by a Canadian Institutes of Health Research (CIHR) Strategy for Patient Oriented Research Catalyst Grant: Patient-Oriented Research. MML is supported by The Ottawa Hospital Anesthesia Alternate Funds Association, a University of Ottawa Junior Research Chair in Innovative Translational Research as well as the Canadian Anesthesia Research Foundation funded Canadian Anesthesiologists’ Society Career Scientist Award. AAM is supported by the Manitoba Medical Services Foundation Dr. F. W. DuVal and John Henson Clinical Research Professorship. Plain English Summary Engaging patients as partners in clinical research, known as patient engagement, is a growing practice that has numerous benefits. However, uptake in preclinical laboratory research (e.g. cell and animal studies) has been limited. Nevertheless, incorporating patients as active collaborators at this discovery stage of biomedical research may be beneficial. To better understand how patient engagement fits into preclinical research, we conducted interviews with patient partners and preclinical researchers who have been early adopters of this practice. Five key themes emerged. First, both groups emphasized the need for adopting a thoughtful and tailored approach since preclinical research is not typically patient facing. Second, shared vocabulary was important to facilitate communication. Third, setting clear expectations and outlining varied goals for engagement was considered critical. Fourth, understanding the value of preclinical research helped ground engagement efforts. Finally, interviewees felt a cultural shift is needed for this practice to be accepted more widely. These themes are important factors to consider when engaging patients in preclinical laboratory research; they may be used to inform and support future preclinical patient engagement efforts.
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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 | 0.191 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".