Proceedings of the 9th International Conference on Digital Public Health
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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 it