Barriers to Translational Research in Windsor Ontario
Bibliographic record
Abstract
Introduction: Translational research is an ideology focused on streamlining the transition of novel research into clinical practice. Central to this approach is overcoming barriers to research involvement and interdisciplinary collaboration. In this study, we survey a variety of professionals in Windsor-Essex to identify barriers to translational research that are important to those working in the community. Method: Using an anonymous close-ended Qualtrics survey distributed via email, we surveyed 88 professionals from Windsor-Essex. This included faculty members form St. Clair College and The University of Windsor (n=59) and clinical care providers (n=46). Results: Managing competing interests, lack of time, funding, infrastructure, and networks were identified by greater than 75% of participants as barriers to research involvement. 62% of physicians identified the lack of permanent post-graduate medical trainees as a barrier. Clinicians were consistently less experienced in research skills compared to others; particularly in publishing results and applying for funding (42% and 40% respectively; p<0.001). Schedule incompatibility, funding issues and identifying interested collaborators with overlapping interests were identified as barriers to interdisciplinary collaboration by 80% of participants. Moreover, 53.8% of those surveyed were unhappy with their research involvement and these individuals were 9% more likely to perceive research as important for their career progression (p=0.046). Conclusion: This study identifies several important barriers to translational research in Windsor-Essex and suggests that many motivated researchers are unhappy with their current involvement. These results will be used by the WE-SPARK Health Institute to inform our ongoing commitment to growing the health research culture in Windsor-Essex.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".