Bibliographic record
Abstract
Nursing is unique among the health-care professions in its view of the patient as a biopsychosocial being. Other health-care disciplines bring a perspective that focuses on one aspect of this triad. For example, medicine as a discipline is based on a biological model of man, psychology is based on theories of cognition and emotion, and medical sociology focuses on the social roles and relationships created by disease and illness. Nursing theoretical models blend a variety of perspectives to reflect the biological, psychological, and social dimensions of the individual. One of nurses' unique contributions to the care of patients and families is the ability to blend these multiple perspectives, both in clinical care and in research. In clinical care, this blending of different dimensions is seen every day. For example, nurses caring for critically ill patients carefully titrate vasoactive medications delivered intravenously to minimize systemic vascular resistance and maximize cardiovascular function, while they simultaneously titrate the information and social support they give to patients and families to minimize anxiety and maximize problem focused coping skills. Cardiac function increases while anxiety decreases. Both are critical to a patient's survival.
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 | 0.004 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.470 | 0.291 |
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".