Bibliographic record
Abstract
Abstract In Norway, data visualisations have been an important form of health reporting for a very long time, going back at least to Statistics Norway's mortality report 1831-1850, which included a shaded map. Social inequalities came up on the Norwegian public health agenda from the 1990s, inspired by the Black report and the Ottawa charter, with several publications that included maps and timelines. Since 2000, the interactive database NorHealth has presented timelines, maps and bar charts illustrating population health and underlying causes for national and regional decision-makers. Indicators on social inequality in health have always been included. The Public Health Profiles (annual from 2012) present health-related data from each municipality in an understandable form, while ongoing projects like Nabolagshelse (‘Neighbourhood health’) aim to add a sustainability perspective. Actual dashboards, defined as single-topic visual representations, have been less common, but after COVID-19, dashboards have been developed within many fields, including National Centre for Ageing and Health's Dementia map, Vestfold county's Sustainability dashboard and Statistics Norway's new visualisations using Tableau Public. The latter demonstrates i.e. social inequality in the use of health services. The Directorate of Health's National digitalisation monitor for the health and care services demonstrates the move towards computer literacy, with dashboards of electronic booking of GP appointments, use of e-prescriptions and production of e-donor cards. Norway's broad participation in JA PreventNCD will lead to more widespread information about how dashboards could be useful in the Norwegian context. The Ministry of Health has ordered a dashboard for the health of older adults (2025). Disclaimer: Contributions to this publication, such as conclusions or other content, made by employees of the Norwegian Directorate of Health, have not been processed within the organisation.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".