Implications of 21st century science for nursing care: interpretations and issues
Bibliographic record
Abstract
The organizing theme for this special volume raises a momentous question: What are the implications of 21st century science for nursing care? The two terms the question relates - 21st century science and nursing care - are each of central importance for nursing and in philosophical enquiry about nursing as a practice, profession, or institution. These key terms are also highly charged and open to interpretation, as is the relationship of implication between them. Different interpretations or assumptions will steer the search for an answer in quite different directions. Putting the expectation of an answer in abeyance, this paper probes the question precisely in its openness to interpretation and contestation. In elaborating different ways in which the main terms of the question can be taken, and analysing how different interpretations of or assumptions about them lead to different responses, the paper delineates crucial issues and divisions in nursing and commentary about nursing today.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| 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".