Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
patients in preparation for surgery; as a public health nurse, I taught mothers to clean their infants’ umbilical cords with alcohol and showed patients newly diagnosed with diabetes how to wipe the skin with alcohol before injecting insulin. Since then, high-quality research has shown that pre-operative shaving increases rather than decreases post-operative infections (Kjonniksen et al. 2002), that cleaning umbilical cords with sterile water shortens the time to cord separation without increasing infections (Medves and O’Brien 1997) and that insulin can be safely injected through clothing (Fleming et al. 1997). These are only three of innumerable examples of how high-quality studies of nursing care can influence our practice. And while it is heartening to know that new evidence is constantly emerging to inform our nursing practice, it is disheartening to learn that many nurses continue to rely on the increasingly dated knowledge they acquired as nursing students (Estabrooks 1998). In this paper, I will describe how high-quality evidence fits into clinical decision-making in nursing practice, and I will call upon key professional groups, such as associations of nursing educators, executive nurses and national nursing organizations, to combine forces and create blue ribbon panels or task forces charged with making recommendations for changes in nursing education and practice that will advance us towards full development as an evidence-based profession.
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.
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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
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 it