Clinical research coordinators’ role in knowledge translation activities in rehabilitation: a mixed methods study
Bibliographic record
Abstract
BACKGROUND: Clinical research coordinators (CRCs) facilitate the interaction between researchers and knowledge users in rehabilitation centres to promote and sustain evidence-informed practices. Despite their presence in rehabilitation settings in Quebec for over 20 years, little is known about their profiles and knowledge translation (KT) activities nor how they can best enact their role. This study explored CRCs' roles and perspectives on the barriers, enablers, and strategies for improving KT activities in rehabilitation settings. METHODS: We conducted a multi-centre, participatory sequential mixed methods study. In the descriptive quantitative phase, we collected data via an online survey to determine CRCs' role in research and KT. In the subsequent qualitative phase, we conducted an in-person focus group to elicit CRCs' perspectives regarding factors influencing their work in KT, and potential solutions for overcoming these challenges. We used a descriptive and an inductive content analysis approach for the data analysis. The data synthesis was inspired by the Promoting Action on Research Implementation in Health Services framework. RESULTS: All nine CRCs from five partner health regions of a large rehabilitation research centre agreed to participate in the study. The data suggest that CRCs are like knowledge brokers and boundary spanners. As information managers, linkage agents and facilitators, CRCs play a pivot role in diffusion, dissemination, synthesis and tailoring of knowledge to improve evidence informed practices and quality of care in rehabilitation. The factors influencing CRCs' KT activities are mostly linked to the context such as the receptivity of the organization as well as the lack of time and resources, and limited understanding of their roles by stakeholders. Two main suggestions made to enhance CRCs' contribution to KT activities include the harmonisation of expectations between the large research centre and their partner health regions, and better promotion of their role to clinical and research teams. CONCLUSIONS: This study provides valuable insights into the scope of CRCs' role. The results shed light on the challenges that they face and potential solutions to overcome them. The knowledge generated in this study can be used to implement this role with similar duties in rehabilitation settings or other health care domains.
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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 | 0.154 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".