A Framework for User Involvement and Context in the Design and Development of Safe e-Health Systems
Bibliographic record
Abstract
Current approaches to health IT research and development emphasize the valuable role of users. However, differences amongst users, in how they are defined, involved and interact with health IT under conditions of varying complexity has received limited attention. Failure to acknowledge these differences makes assessments of the quality, reliability and transferability of results problematic. More importantly, as e-health systems are increasingly opened up to use by health consumers the implications of differences in the context of system use for patient safety require closer investigation. To support the safety of e-Health systems, it is essential that where users are involved we can more accurately differentiate between types of users and their contexts of use and how these factors interact with usability and the risk of unintended consequences from such systems. This paper presents an extended three dimensional user-task-context matrix for considering who users of healthcare applications are, their needs and their requirements under differing contexts of use.
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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 | 0.025 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".