Context Sensitive Health Informatics:Human and Sociotechnical Approaches
Bibliographic record
Abstract
Healthcare information technologies are now routinely deployed in a variety of healthcare contexts. These contexts differ widely, but the smooth integration of IT systems is crucial, so the design, implementation, and evaluation of safe, effective, efficient and easy to adopt health informatics involves careful consideration of both human and organizational factors. This book presents the proceedings of the Context Sensitive Health Informatics (CSHI) conference, held in Copenhagen, Denmark, in August 2013. The theme of this year’s conference is human and sociotechnical approaches. The Human Factors approach is distinctly design-driven and aims to optimize performance, safety and users’ sense of well-being associated with their use of a system through the application of user-centered systems design and evaluation. The papers and presentations included here are grouped under the topics: patients and IT; usability test and evaluation; work tasks and related contexts; human factors and simulation; and context and systems design, and outline theories and models for studying contextual issues and insights related to how health information technologies can be better designed to accommodate different healthcare contexts. Healthcare organizations, health policy makers and regulatory bodies globally are starting to acknowledge this essential role of human and organizational factors for safe and effective health information technology. This book will be of interest to all those involved in improving the quality of healthcare worldwide
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".