Work in progress — A smartphone application as a teaching tool in undergraduate nursing education
Bibliographic record
Abstract
One in four people in a healthcare facility has a pressure ulcer (bedsore) at any given time, and bedsores are one of the leading iatrogenic causes of death reported in developed countries. Standardized documentation is identified as a critical component in the prevention and treatment of pressure ulcers, with the greatest challenges being non-compliance to protocol and inconsistency of documentation. As a result, attention is focused on electronic information systems, and the research objective in this work was to develop an interactive software application on a mobile device (Smartphone; tablet) to allow healthcare workers to electronically document patients' wounds, and to explore whether the application may promote higher consistency and compliance in wound care documentation, and higher patient and caregiver satisfaction relative to paper-based documentation. A prototype application on an Android platform is in progress with additional intelligence over paper-based forms. The prototype is being extended to a version designed as an educational tool for undergraduate nursing students learning clinical practices in wound care. The work advances the emerging area of healthcare applications and supports the increasing prevalence of e-health in nursing practice.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".