Overcoming Challenges to Support Clinician-Scientist Roles in Canadian Academic Health Sciences Centres
Bibliographic record
Abstract
Clinician-scientists (CSs) make significant contributions to the healthcare system, yet their roles are not fully understood, supported or recognized by healthcare leaders or policy makers. CSs are healthcare professionals with advanced research training who continue to pursue clinical work and are considered an essential component of the research infrastructure in academic health sciences centres. The current literature supports the role of CSs but is also clear that there are multiple challenges in attracting and retaining clinicians to the role. To gain a comprehensive understanding of the current status of the CS role, two literature reviews were conducted. The findings reported here include an overview of: the education and training preparation for CS roles; the importance of the CS role; barriers and challenges to developing and implementing the CS role; and strategies for supporting and sustaining CS roles in practice. The paper further describes one Canadian academic health sciences centre's approach to supporting and increasing the number of CSs from nursing and allied health professions to support academic practice. Non-physician CSs may conduct research using multiple research designs across the research continuum from randomized controlled trials to grounded theory or qualitative descriptive approaches. Their research generally focuses on practice-based issues such as best practices for managing pain or frailty or evaluating the effectiveness of new approaches to care. Researchers and healthcare leaders in other organizations may find this work helpful for establishing their own structures to enhance research capacity and practice-based research, especially for non-physician CSs.
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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 | 0.053 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| 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".