Bibliographic record
Abstract
Description I love the statement, "Nursing is an art and a science." I practiced at the bedside for 30 years, caring for my patients physically and mentally through all of life's stages. Quilting became my go-to activity when the work became too stressful and I needed to decompress. When I became a nurse educator, I made this quilted wall hanging for my office to remind me of those years and the reasons I became a nurse. The nurse applique was made from one of my scrub tops and replicates my work uniform, down to the ponytail I wore when I was younger, and my maroon stethoscope. It also includes nursing fabric that I have collected over the years, a bandana from the American Association of Critical-Care Nurses' National Teaching Institute and a t-shirt I received as a gift when I was getting my Master's degree in nursing education. When I look at the quilt, I remember all the hard work of nursing school, the uncertainty during my first year as a nurse, and the changes in myself as I grew from novice to expert. I think of the patients we lost, the ones we saved, the family members that I laughed with, and those that I cried with. Nursing really chose me, not the other way around, and I am proud to say that I answered that call.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.044 | 0.022 |
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".