Building a Collaborative Translational Research Platform: Identifying Barriers and Enablers From Basic Research to Primary Healthcare
Bibliographic record
Abstract
Introduction The capacity to translate basic research discoveries into clinical applications and to synthesize, disseminate, and integrate clinical research results into practice remains challenging. To help innovate the means of communicating and disseminating knowledge between actors across the research-to-practice continuum, this study aims to identify barriers and enablers in building and implementing a collaborative platform that will bring together all the actors involved. Methods The study was conducted based on a qualitative descriptive design and a deductive thematic analysis. Recruitment was performed using a purposive sampling strategy. Data were collected through three focus groups with a total of 23 participants involving actors from each pillar of the research-to-practice continuum: eight basic researchers (Group 1), eight clinical and organizational researchers (Group 2), and seven knowledge users, including healthcare professionals and patient partners (Group 3). Results Few participants had concrete experience in the field of translational research, but half of them had already collaborated with actors from other research pillars. Identified barriers (e.g., length and complexity of the process, differences in knowledge and professional goals between clinical and basic research, insufficient resources and time to invest in research projects, lack of recognition of the added value of patient implication) and enablers (e.g., use of clear guidelines and targeted research questions, networking by matching according to area of practice and interests, introduction to research in the curriculum of medical students, dissemination of scientific information in a language understandable to all) emerging from the focus groups were clustered into four main categories: (i) translational research project concretization, (ii) basic research applicability, (iii) clinician availability and commitment, and (iv) patient involvement and recruitment. These barriers and enablers emphasized the need to decomplexify the translational research process for all actors involved in the research-to-practice continuum. They also accentuated the need to recognize the social responsibility of basic research and increase its impact on the ground, intensify the exposure of medical students to research and value clinicians' involvement in research activities, and engage patients as research partners to help prioritize research topics and communicate science comprehensibly. The success indicators of the main platform would be the relevance, strength, and duration of collaborations, the number of projects implemented and completed, and the grants and funding obtained. Conclusion We identified key barriers and enablers to implement a functional and dynamic collaborative platform. Participants' desire to collaborate combined with the absence of a tool to foster efficient collaborations are indicators of the importance of our approach. Creating a readily accessible collaborative platform to all actors across the research-to-practice continuum has the potential to drive research advances based on the needs of patients, clinicians, and the public, and thus facilitate their clinical application in a timelier manner to improve healthcare standards and services as well as population health outcomes.
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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 | 0.275 | 0.329 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.005 | 0.041 |
| Research integrity | 0.005 | 0.007 |
| 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; 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".