Proceedings of the 9th International Conference on Digital Public Health
Bibliographic record
Abstract
Warm welcome to the 9th International Conference on Digital Public Health (www.acmdigitalhealth. org). This year, the DPH committee agreed to rebrand the conference 'Digital Public Health' more accurately represents the focus of the event on public health, and highlights the niche status of DPH. Held on 20th - 23rd November 2019 in Marseille, France, the DPH 2019 is supported by the newly established UCL IRDR Centre for Digital Public Health in Emergencies (dPHE) and for the first time it is being haeld in conjunction with a public health event rather than a computer science venue. We are delighted to join forces with the 12th European Public Health Conference 2019 and continue our cooperation with ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD). We look forward to two parallel tracks on digital health: the 9th DPH 2019 conference with technical focus, and a joint track with EPH 'Digital Applications in Health' bringing public health applications of digital health. Young researchers, MSc and PhD students will enjoy a truly interdisciplinary 'Young Researches Forum' day organised in collaboration with ASPHER. Building on the growing success of previous editions (2008 London, 2009 Istanbul, 2010 Casablanca, 2011 Malaga, 2013 Rio de Janeiro, 2014 Soul, 2015 Florence, 2016 in Montreal, 2017 London, 2018 Lyon), the 9th International Digital Public Health mission has ideally met the EPH 2019 vision: 'Building Bridges for Solidarity and Public Health'. We are proud to be celebrated as a unique prime interdisciplinary venue with world class networking opportunities highly praised by participants every year. A DH 2017 participant highlighted: "This has been an amazing conference. So many interesting people and presentations. More importantly, it's been like meeting a group of friends". A DH 2018 participant commented: "I learnt a lot about digital data analysis going on, and serious gaming. We are now exploring a new project as a direct result of this conference." From a small interdisciplinary scientific conference bringing together IT researchers and health professionals, DPH has grown to fully embrace the third stakeholder group: the start-ups and innovators in digital health offering the popular Digital Health Innovation Award 2019 in two categories. With a focus on public health, global health, social media, big data analytics, pandemics preparedness and humanitarian digital health; the DPH 2019 offers even more: joint hands-on session on Missing Maps organised by British Red Cross and Medicines Sans Frontiers, as well as a joint EPH and RECON workshop offering a session on programming in R for epidemiologists.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.242 | 0.146 |
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".