Special issue on software engineering for Connected Health: Challenges and research roadmap
Bibliographic record
Abstract
Abstract Over the past decade, there have been increasing expectations and pressures placed on health care providers to deliver more efficient, quality, and safe health care services. As a result, this shifts the balance between supply‐and‐demand of health care services. It also brings about new challenges for health care professionals' capabilities to deliver safe and quality care in a timely manner. There are significant opportunities to exploit information and communications technology and transform how health care service is provided. Connected Health is one such transformation for health care management and changes in health care practice. However, the field of Connected Health is still in its infancy. This Special Issue in Software Engineering for Connected Health begins to address this and presents 5 quality contributions to demonstrate how software engineering research plays an important role in Connected Health research. These contributions also identify the limitations of the existing theories and to develop new or revised theories of Software Engineering for Connected Health.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".