A standardised mobile internet (wireless) environment for mobilising healthcare
Bibliographic record
Abstract
Healthcare has become one of the foremost domestic issues in the USA; healthcare costs represent about 14% of the Gross Domestic Product. Business and industry view the increased costs of healthcare and healthcare benefits as impediments to their ability to compete in international markets. Various health organisations and researchers are trying to get relevant research findings and successful innovations and management practices from other industries, countries, and healthcare organisations. This chapter discusses the findings from INET's study on mobile internet (wireless) technology initiatives in healthcare by Ontario Hospitals in Canada. This research has shown that mobile/wireless solutions for healthcare can achieve four critical goals to: (1) improve patient care; (2) reduce transaction costs; (3) increase healthcare quality; and (4) enhance teaching and research. Integral to the incorporation of wireless initiatives is the reliance on the healthcare portal and the underlying three-tier web based architecture. The same or similar initiatives can be exploited by other hospitals to incorporate a wireless/m-commerce solution to enable hospitals to operate effectively and efficiently in today's competitive and costly healthcare environment. Copyright © 2003 Inderscience Enterprises Ltd.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".