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Record W4416791451 · doi:10.1186/s40900-025-00818-9

Identifying challenges and enablers to engaging patients in preclinical laboratory research: an interview study

2025· article· en· W4416791451 on OpenAlexafffund
Madison Foster, Dean Fergusson, Emily Thompson, Victoria Hunniford, Talston Scott, Stephen R. Daniels, Dawn P. Richards, Pat Messner, Kathryn Hendrick, Patrick Sullivan, Asher A. Mendelson, Kimberly F. Macala, Kirsten M. Fiest, Angela M. Crawley, Bernard Thébaud, Stuart G. Nicholls, Cheryle A. Séguin, Grace Fox, Justin Presseau, Manoj M. Lalu

Bibliographic record

VenueResearch Involvement and Engagement · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsWestern UniversityInstitute of Infection and ImmunityUniversity of CalgaryAlberta Health ServicesUniversity of AlbertaOttawa HospitalUniversity of ManitobaCARE CanadaGlycemic Index LaboratoriesRoyal Alexandra HospitalUniversity of Ottawa
FundersOttawa Hospital Anesthesia Alternate Funds AssociationStem Cell NetworkManitoba Medical Service FoundationCanadian Institutes of Health ResearchCanadian Anesthesia Research Foundation
KeywordsThematic analysisTheme (computing)Qualitative researchPatient experienceInterpretation (philosophy)Vocabulary

Abstract

fetched live from OpenAlex

Patient engagement in research enriches study design, conduct, and dissemination by integrating lived experiences of patients into the research process. Although patient engagement is becoming more popular in clinical research settings, it remains comparatively rare in preclinical (i.e. laboratory based) research. To explore this gap, we conducted an interview study to understand how researchers and patients have implemented patient engagement in this area, focusing on the challenges and benefits of their approach. We conducted semi-structured interviews of patients (n = 15) and researchers (n = 14) with previous preclinical patient engagement experience. Interviews were transcribed and reviewed using an inductive, thematic content analysis, which allowed for bottom-up analysis of interview data. Our team identified, reviewed and refined emerging themes. Our team members include preclinical, clinical and patient engagement researchers and patient partners, which allowed for various perspectives to contribute to the final interpretation of the findings and drafting of the manuscript. We identified five themes. Theme 1: Researchers and patients highlighted the necessity to adopt a thoughtful and tailored approach for each preclinical engagement initiative. This includes taking time to cultivate personal relationships and co-developing engagement activities to meet patient and researcher preferences and needs. Theme 2: Clear communication was deemed critical, suggesting the need for a clear and shared vocabulary without technical jargon. Theme 3: Varied goals for engagement in preclinical research between researchers and patients were underscored, indicating the need to discuss aims and motivations early and often as well as to co-develop mutually beneficial strategies. Theme 4: Researchers and patients also discussed how their communities require a better understanding of the value of preclinical patient engagement. This could be fostered through education and illustrative case examples. Theme 5: Finally, a shift in research culture was deemed necessary 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. 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. 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 implemented 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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.063
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0630.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.878
GPT teacher head0.644
Teacher spread0.234 · 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

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
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

Citations2
Published2025
Admission routes2
Has abstractyes

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