Implementing the Co-Immune Open Innovation Program to Address Vaccination Hesitancy and Access to Vaccines: Retrospective Study
Bibliographic record
Abstract
BACKGROUND: The rise of major complex public health problems, such as vaccination hesitancy and access to vaccination, requires innovative, open, and transdisciplinary approaches. Yet, institutional silos and lack of participation on the part of nonacademic citizens in the design of solutions hamper efforts to meet these challenges. Against this background, new solutions have been explored, with participatory research, citizen science, hackathons, and challenge-based approaches being applied in the context of public health. OBJECTIVE: Our aim was to develop a program for creating citizen science and open innovation projects that address the contemporary challenges of vaccination in France and around the globe. METHODS: We designed and implemented Co-Immune, a program created to tackle the question of vaccination hesitancy and access to vaccination through an online and offline challenge-based open innovation approach. The program was run on the open science platform Just One Giant Lab. RESULTS: Over a 6-month period, the Co-Immune program gathered 234 participants of diverse backgrounds and 13 partners from the public and private sectors. The program comprised 10 events to facilitate the creation of 20 new projects, as well as the continuation of two existing projects, to address the issues of vaccination hesitancy and access, ranging from app development and data mining to analysis and game design. In an open framework, the projects made their data, code, and solutions publicly available. CONCLUSIONS: Co-Immune highlights how open innovation approaches and online platforms can help to gather and coordinate noninstitutional communities in a rapid, distributed, and global way toward solving public health issues. Such initiatives can lead to the production and transfer of knowledge, creating novel solutions in the public health sector. The example of Co-Immune contributes to paving the way for organizations and individuals to collaboratively tackle future global challenges.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".