mHealth: Technology for nursing practice, education, and research
Bibliographic record
Abstract
Mobile health (mHealth) is a rapidly growing field providing the potential to enhance patient education, prevent disease, enhance diagnostics, improve treatment, lower health care costs and increase access to health care services, and advance evidence-based research. For the field of nursing the potential capabilities of mHealth are not only for patient care but for delivery of nursing education to our future practicing nurses, providing a means of communication between healthcare professionals located close and at greater geographic distances, and provides access to information and personal monitoring for geographically isolated clients. Although mHealth capabilities’ value appears significant for training, and practice, there remains a significant need for research and evaluation of the devices that now appearing in the health care marketplace. The National Institute of Nursing Research’s strategic plan includes supporting research to develop and test the flood of health apps to assist clients in the management of their health. The purposes of this paper are to: 1) discuss the importance of mHealth in nursing practice, education, and research, and 2) describe the mHealth initiatives underway at the University of Pittsburgh School of Nursing as exemplars to stimulate mHealth research and promote nursing role in providing health care to patients in this age of information technology.
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 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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.024 |
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".