How does venture capital operate in medical innovation?
Bibliographic record
Abstract
While health policy scholars wish to encourage the creation of technologies that bring more value to healthcare, they may not fully understand the mandate of venture capitalists and how they operate. This paper aims to clarify how venture capital operates and to illustrate its influence over the kinds of technologies that make their way into healthcare systems. The paper draws on the international innovation policy scholarship and the lessons our research team learned throughout a 5-year fieldwork conducted in Quebec (Canada). Current policies support the development of technologies that capital investors identify as valuable, and which may not align with important health needs. The level of congruence between a given health technology-based venture and the mandate of venture capital is highly variable, explaining why some types of innovation may never come into existence. While venture capitalists' mandate and worldview are extraneous to healthcare, they shape health technologies in several, tangible ways. Clinical leaders and health policy scholars could play a more active role in innovation policy. Because certain types of technology are more likely than others to help tackle the intractable problems of healthcare systems, public policies should be equipped to promote those that address the needs of a growing elderly population, support patients who are afflicted by chronic diseases and reduce health disparities.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".