Barriers to Surgical Innovation Research: A Canadian Study on Public Funding Trends
Bibliographic record
Abstract
BACKGROUND: A decline in research funding has been cited as a potential cause for limited surgical innovation in the United States. We aim to understand if this is a North American phenomenon and explore whether a lack of public funding is a barrier to surgical innovation in Canada. METHODS: Publicly available funding data from Canadian Institutes of Health Research (CIHR) were reviewed from 2008 to 2019 to determine the yearly funding distributed to surgical departments. Surgical innovation studies were identified and total yearly funding was calculated. All amounts were adjusted for inflation to reflect 2019 Canadian dollar value. RESULTS: From 2008 to 2019, surgical departments were granted 1.82-4.70% of total CIHR funding. In total, 902 grants were allocated to surgical departments and 126 (14.0%) met criteria for surgical innovation. Surgical innovation research was allocated a total annual amount ranging from 1.52 to 9.01 million CAD. There appears to be an upward trend in public funding for surgical innovation over this time period. DISCUSSION: Contrary to the landscape in the United States, there is no evidence of decreasing trends in public funding for surgical innovation in Canada. Considerations should be given to other potential barriers precluding surgeons from participating in innovation. CONCLUSION: Only a small percentage of research dollars to departments in Canada are spent on innovation research, despite an overall increasing trend in total public research funding over the past 10 years. We need to foster an environment in which surgical innovation is encouraged through medical curriculum changes, multidisciplinary collaboration opportunities, and dedicated faculty resources.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.024 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".