Where is Nursing in the Electronic Health Care Record?
Bibliographic record
Abstract
The authors explore the possibilities for documenting professional nursing practice in an electronic health record. Recognizing that there are a variety of approaches to electronic documentation, the intent of this discussion is to generate a general rather than a particular approach to this issue. Nurses themselves must determine the ways in which professional nursing care will be captured in the electronic systems used in their facilities. Questions that arise from nursing include: How can nurses balance generalized care and protocol management with the need for documentation of each individual's nursing needs and particular experiences? How can the goals of nursing care be incorporated into the record? How can nursing actions/interventions be clearly communicated to all members of the health care team? In what ways can an electronic record document collaboration with the client to determine individualized outcomes of care and treatment? In considering these questions a number of issues arise: the selection of standardized languages to be used in the records, the title of the record, the tension between coding and text, the accessibility and transferability of the record, the ability to retrieve data on nursing outcomes through data mining techniques, ownership of the record, and privacy/security of the information stored. Although the paper will make no attempt to answer these questions it will draw on relevant journal articles to provide a context for this pivotal change in that way we account for health care practice.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".