93 Investigating the minimal requirements for startup procurement by healthcare institutions in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES/GOALS: The primarygoal is to understand the challenges and barriers associated with the procurement of innovative technologies.Specifically, our research will answer the following question: what are the minimal requirements for a startup’s solution to beprocuredby anOntariohealthcare institution? METHODS/STUDY POPULATION: Participants will include procurement professionals at startups, healthcare institutions, and procurement facilitating agencies. Semi-structured interviews will be conducted in order to understand different procurement pathways and the possible procurement related gaps or barriers that startups face. Through qualitative ethnographic methods, participant interviews will characterize existing relationships and examine the rationale behind startup procurement decision-making. Data collection will include recordings, verbatim transcripts, and researcher field notes. Through inductive qualitative analysis, the data will be examined to build an intervention to assist in startup procurement. RESULTS/ANTICIPATED RESULTS: Our investigation will yield insight into expectations between hospital procurement requirements and startup procurement. The qualitative analysis will identify targets for engagement, and appropriate actors that can bridge gaps. Our results will identify pathways for procurement and the minimal procurement requirements to aid startup procurement planning. Our research will support innovators by delivering an intervention that will enable easier implementation of market ready solutions in a Canadian context. In line with principles from the National Center for Advancing Translational Sciences, this research can be used towards enhancing efficiency, speed of translation, and innovation. DISCUSSION/SIGNIFICANCE: We will contextualize the needs of start-ups and empower them to understand their procurement ecosystem. Facilitating better navigation of the procurement space allows for innovators to present solutions that healthcare organizations can adopt, resulting in improved clinical and patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".