Context Sensitive Health Informatics and the Pandemic Boost:All Systems Go!
Bibliographic record
Abstract
The COVID-19 pandemic has accelerated the pace at which innovative health technologies are being designed, developed, and implemented. This inevitably presents new risks and challenges, not least, how to ensure that these technologies are appropriate for particular environments. In this sense, ‘environments’ may be people in various roles (e.g. patients, users, designers, evaluators) or non-human constructs such as organizations, work practices, guidelines and protocols, buildings, and markets. This book presents papers from CSHI 2023, the latest in the series of biennial conferences on Context Sensitive Health Informatics, held in Sydney, Australia, on 5 and 6 July 2023. The theme of CSHI 2023 was Context Sensitive Health Informatics and the Pandemic Boost, and the book includes 19 papers and 7 poster abstracts covering a variety of topics. These are divided into 5 sections: clinician perceptions and use of health technologies; workforce development in health informatics; aligning workflows and work systems to health technologies; co-design, equitable evaluation, and sustainable implementation of digital health tools; and big data and information management. The book provides an overview of the latest health information systems and of recent research in the area of context and health information technologies, and will be of interest to all those working in the field of health informatics.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".