Barriers to translational research in Windsor Ontario: a survey of clinical care providers and health researchers
Bibliographic record
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 < 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, 46% of those surveyed were unhappy with their research involvement and these individuals were 13% more likely to perceive research as important for their career progression (p = 0.244). CONCLUSIONS: 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 inform decision making in the research community of Windsor-Essex and provides insight for communities of similar size and research capacity. Ultimately, enabling the translation of clinical research in all communities is required to ensure equitable access to cutting edge care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".