Barriers to translational research in Windsor Ontario: a survey of clinical care providers and health researchers
Bibliographic record
Abstract
Abstract Background Translational research is an ideology focussed on streamlining the transition of novel research into clinical practice to ultimately benefit populations. Central to this approach is overcoming barriers to research involvement and interdisciplinary collaboration. Identifying barriers has been the subject of several studies focused on communities with large academic hospitals. The Windsor-Essex region is currently built around community hospitals which have less of an emphasis on research, employ fewer physicians holding academic appointments and generally do not provide incentivised time for research and training. In this study, we surveyed clinicians and researchers working in Windsor-Essex to gain insight into barriers to translational research important to those working in smaller sized, community-based research networks. Methods Using an anonymous close-ended Qualtrics survey distributed via email, we surveyed faculty members from The University of Windsor and clinical care providers from Windsor-Essex (n = 68). This included 24 physicians, 14 allied health professionals, and 30 non-clinician researchers. 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 (p
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".