Bibliographic record
Abstract
Background The Danish eHealth portal, sundhed.dk, pioneers open access to medical records since 2003 and is, in this regard, unique worldwide. As part of the Danish health care sector, sundhed.dk supports transparency and patient empowerment and provides health professionals with the possibility to access patient health data outside of local systems and across sectors and boundaries. Sundhed.dk and the MyHealth app have played a crucial role during the COVID-19 pandemic in Denmark, not least due to the development of the corona passport. Objective The overall purpose during the COVID-19 pandemic has been to assist the health authorities handling the pandemic and to support patient empowerment. Sundhed.dk provided new digital services to support monitoring the spread of the virus and telemedicine solutions to support the Danish citizens both during isolation and as the society was gradually reopening. Methods By exploiting sundhed.dk’s known and widespread position in the society and the already existing digital building blocks, sundhed.dk and MyHealth managed to provide fast and easy access to COVID-19 laboratory responses and, on top of that, to develop the corona passport, which was the result of accelerated IT development and fast scaling of the required server with the users. Results The many digital services accessible through sundhed.dk or MyHealth made it possible for Danish citizens to continue to see their doctor or physiotherapist during lockdown. Moreover, the corona passport made the safe and efficient reopening of Denmark possible. Conclusions The story of sundhed.dk during the COVID-19 pandemic is a story of success, a result of its close interdisciplinary and cross-sectoral cooperation with Danish health authorities and private IT vendors. It illustrates what can be achieved when there is a unified blueprint and overall purpose to overcome barriers in order to ensure development and progress for both the individual and the society. This has also drawn global attention, as reflected in a report by the World Health Organization.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".