Scientific Global Nursing Hackathon Experience
Bibliographic record
Abstract
BACKGROUND: Hackathons are organized to bring together both experienced and novice individuals from a variety of backgrounds to brainstorm creative solutions to complex issues. Hackathons may last from a few hours to a few days and may provide rewards for winning entries. PURPOSE: In this article, we describe an experience with a scientific hackathon at an international nursing research congress in Calgary, Canada. We discuss the purpose, process, benefits, and challenges of this hackathon. APPROACH: For this article, we have used a descriptive approach. OUTCOMES: The scientific hackathon experience united international nursing scholars into a community with a common focus enabling the continuation of mutual, future endeavors. CONCLUSION: Hackathons are a means of connecting novices and experts from different backgrounds to develop technology-based solutions for health care issues. The ideas generated at hackathons may be further developed to bring the project to fruition to positively impact health care.
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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".