An innovation procurement clinical framework: A qualitative study
Bibliographic record
Abstract
Innovation Procurement Strategies (IPS) strive for purchasing healthcare solutions that do not yet exist on the market and are increasingly being advocated to improve health outcomes while managing escalating healthcare costs. Due to the newness of IPS, there are limited resources available to healthcare organizations and professionals looking to engage in IPS. The purpose of this study was to develop an evidence-based clinical framework to guide healthcare organizations and professionals. Adopting a qualitative grounded theory approach, we interviewed participants with experience in innovation procurement to understand the skills, resources, and supports needed to initiate and oversee an IPS project. Using thematic design and open coding, three overarching themes emerged from the data and formed the basis of our IPS clinical framework. By describing the components, skills, and supports and resources necessary for engaging in IPS, our framework addresses the knowledge gap in healthcare organizations and professionals wishing to implement IPS.
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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 | 0.041 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| 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".