Bibliographic record
Abstract
Although we all recognize when we are being cared for or treated in a caring way, the term care not easy to define. We feel that words comfort and care have slightly different meanings, yet their differences are not easy to pin down. To Dr. Janice Morse, a professor of nursing at the University of Alberta, these subtleties are important. She conducting a major research project funded by the U.S. National Center for Nursing Research, NIH, to examine the concepts of comfort and caring in nursing. For her, an understanding of caring and comfort important because care is essential to keep families and societies and cultures and nations and human kind together, and because historically, care has been seen as the essence of nursing. Since the early twentieth century, with the development of scientific medicine and a medical profession, the two functions of curing and caring were split. According to Barbara Ehrenreich and Deirdre English in a book which summarizes the history of women healers, Curing became the exclusive province of the doctor; caring was relegated to the nurse. All credit for the patient's recovery went to the doctor and his 'quick fix'. The nurse's activities, on the other hand, were barely distinguishable from those of a servant. She had no power, no magic, and no claim to the credit. Dr. Morse believes that nurses indeed deserve credit for the patient's recovery, and that one of their major contributions, that is, caring, a necessary element in the curing process.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.013 | 0.028 |
| Insufficient payload (model declined to judge) | 0.013 | 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".