Clinician-Scientists in Canada: Barriers to Career Entry and Progress
Bibliographic record
Abstract
BACKGROUND: Clinician-scientists play an important role in translating between research and clinical practice. Significant concerns about a decline in their numbers have been raised. Potential barriers for career entry and progress are explored in this study. METHODS: Case-study research methods were used to identify barriers perceived by clinician-scientists and their research teams in two Canadian laboratories. These perceptions were then compared against statistical analysis of data from Canadian Institutes of Health Research (CIHR) databases on grant and award performance of clinician-scientists and non-clinical PhDs for fiscal years 2000 to 2008. RESULTS: Three main barriers were identified through qualitative analysis: research training, research salaries, and research grants. We then looked for evidence of these barriers in the Canada-wide statistical dataset for our study period. Clinician-scientists had a small but statistically significant higher mean number of degrees (3.3) than non-clinical scientists (3.2), potentially confirming the perception of longer training times. But evidence of the other two barriers was equivocal. For example, while overall growth in salary awards was minimal, awards to clinician-scientists increased by 45% compared to 6.3% for non-clinical PhDs. Similarly, in terms of research funding, awards to clinician-scientists increased by more than 25% compared with 5% for non-clinical PhDs. However, clinician-scientist-led grants funded under CIHR's Clinical thematic area decreased significantly from 61% to 51% (p-value<0.001) suggesting that clinician-scientists may be shifting their attention to other research domains. CONCLUSION: While clinician-scientists continue to perceive barriers to career entry and progress, quantitative results suggest improvements over the last decade. Clinician-scientists are awarded an increasing proportion of CIHR research grants and salary awards. Given the translational importance of this group, however, it may be prudent to adopt specific policy and funding incentives to ensure the ongoing viability of the career path.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".