Bibliographic record
Abstract
Health care is an industry with a diverse set of stakeholders: governments, private health care providers, medical practitioners (physicians, nurses, researchers, etc.), home health care providers and workers, and last but not least, clients/patients and their families. Overlapping and interacting environments include hospitals, clinics, long-term care facilities, primary care providers, homes, and so forth, involving acute, emergency, chronic, primary, and outpatient care. Patient transitions between these environments are often unnecessarily dif?cult due to an inability by providers to access pre-existing patient records. Mobile/wireless solutions can play an important role in supporting health care by providing applications that access health care records and reduce paperwork for clinical physicians, nurses, and other workers, community health care practitioners and their patients, or mobile chronically ill patients such as diabetics. This chapter makes the case for mobile health care and its solutions in the non-acute community health care environment, where critical issues include usability, adoption, interoperability, change management, risk mitigation, security and privacy, and return on investment. A proposed community health care application demonstrates how these issues are addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.008 |
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; both teacher heads agree on what is shown here.
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".