Toward a holistic definition for Information Systems for Health in the age of digital interdependence
Bibliographic record
Abstract
The article's main objective is to propose a new definition for Information Systems for Health, which is characterized by the identification and involvement of all the parts of a complex and interconnected process for data collection and decision-making in public health in the information society. The development of the concept was through a seven-step process including document analysis, on-site and virtual sessions for experts, and an online survey of broader health professionals. This new definition seeks to provide a holistic view, process, and approach for managing interoperable applications and databases that ethically considers open and free access to structured and unstructured data from different sectors, strategic information, and information and communication technology (ICT) tools for decision-making for the benefit of public health. It also supports the monitoring of the Sustainable Development Goals and the implementation of universal access to health and universal health coverage as well as Health in All Policies as an approach to promote health-related policies across sectors. Information Systems for Health evolves from preconceptions of health information systems to an integrated and multistakeholder effort that ensures better care and better policy-making and decision-making.
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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 | 0.021 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".