Valorization in public health genomics : a conceptual development from technology transfer to healthcare integration
Bibliographic record
Abstract
facilitate this framework include interactions of people from multiple disciplines for example, basic and applied university researchers, industrial partners, hospitals, contract researchers, venture capitalists, funding agencies including both non and governmental, human resource department, policy makers and local government.The expectation of the stakeholders within this framework is consistent and clear communication and interactions between the different players involved to facilitate knowledge valorization. Open InnovationsAnother term related to valorization is open innovation.Open innovation [24] can be defined as to profit from external knowledge without making heavy internal investment in long term research.This may include any form of cooperation with third parties that can contribute to improve the long term performance of a company as well as grants both governmental and NGOs.Cooperation with third parties, for example contracted research to private institutes, companies or universities will help in avoiding substantial investment in one's own infrastructure to do research to develop say a product.Licensing production is another example.Stakeholders expect minimal amount of investment with m ax i m al am o u nt o f r e t ur ns .C o nsi s t e nc y c an v ar y f r o m c ase t o c as e as w el l as o r g ani z at i o n t o organization.Current trends (patents, trademarks and trade secrets among others) limit the full potential for open innovation.Technology Transfer Valorization also closely associates with another term called Technology Transfer (TT).Technology Transfer aims to transfer technology from one organization to another organization.TT is seen as an activity of the migration of early discoveries in any setting (e.g.private sector, academia) to useful application in the development of marketable products or processes (adapted from [25]).Again this is on the business side of translational research and overlaps with the concept of valorization but generally has defined methods.Various TT methodologies are well known and actively utilized by the commercial sector to move ideas from the lab onto the market.For an example see figure 1 [26].Furthermore, TT offices exclusively also exist both in academics as well as private sector just to help in commercializing an idea or patent or technology.Other resources include investors as well as development of business models.Staff activities can involve coming up with a concept which addresses a market need and subsequently developing that concept.The concept of TT is well established and proven [27].Stakeholders expect that the product developed through the TT pipeline is eventually rolled into the The Public Health Genomics Enterprise The Public Health Genomics (PHG) Enterprise (see figure 6 below) is a composite 'for effective translation of genome-based knowledge and technologies into improved population health' [51].The consensus was developed by an international expert workshop held in Bellagio, Italy in 2005, with 18 experts from US, Canada, Germany, UK and France.Although not part of the assessment process by policy makers, it nonetheless contains important components and overlaps with tools previously mentioned.The PHG Enterprise's knowledge integration is considered pivotal [51].It can be defined as 'the process of selecting, storing, collating, analyzing, integrating and disseminating genome-based information both within and across disciplines for the benefit of population health'.This can also constitute methodological progression as well [51].Further information can be seen from figure 6 below.F i g u r e 7 : The Public Health Wheel which divides 10 essential tasks of public health over the domains of assessment, policy development and assurance with research at its core.The idea is that by addressing these 10 essential tasks, integration of genomics into public health can be possible.Taken from [52].
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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 | 0.027 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.044 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".