Mobile Solutions for Managing Health Care
Bibliographic record
Abstract
Wirhe project is an international collaborative study that focused on the future of healthcare needs, technology requirements and solutions for effective use of wireless platform for health care delivery. In this chapter, the authors present results of a Wirhe survey of 85 experts and individual interviews with 35 experts. The authors asked their opinions on the current status of adopting wireless equipment in health care, unmet needs in serving hospital in-patients and outpatients, and their views on the incorporation of wireless platform for future health care delivery and personal health management. Key findings are that 1) both remarkable quality improvements and process enhancements can be expected from thoroughly utilizing the wireless technologies and mobile solutions, 2) integration of personal health monitoring and professional health management is a key issue to be addressed and 3) health promotion and illness prevention will grow by utilizing mobile solutions. As a result of this study, they propose a framework that can be used in developing wireless health care solutions for managing diseases and related health problems. It can also be used to structure and stratify the needs by importance and utility, to anticipate which technologies and solutions are needed next, and to estimate how large the market size may be for industries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".