Building a Collaborative Translational Research Platform: Identifying Barriers and Enablers From Basic Research to Primary Healthcare
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,275 | 0,329 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,014 | 0,016 |
| Communication savante | 0,021 | 0,020 |
| Science ouverte | 0,005 | 0,041 |
| Intégrité de la recherche | 0,005 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».