Revolution of Nursing Science in the Next Decade
Bibliographic record
Abstract
Knowing a history of nursing science allows nurses to understand the journey of nursing theorists and scientists in developing the nursing science. Since the era of Florence Nightingale in the mid-18th century, the nursing theories and nursing research were published to the public, particularly scientific community. During late 18th and 19th century, there were many interesting questions among these debates that have not been answer yet, such as is it a nursing science or science of nursing? and is it a pure science or applied science? Even though the scientific community acknowledges that nursing is known as a science, many scholars remain to discuss the definition and category of nursing science. Along the history of nursing knowledge development, nursing scholars faced many challenges concerning the difficulty to develop the required content for nursing practices, to be an excellent discipline in the developing science base, to prepare nurse researchers, and to disseminate research results to the community and to the public (Hinshaw, 1989). Nowadays nursing science is entering into the postmodern era and it will be continued development in the next decade. Even if nurses do not recognise that nursing knowledge is currently in crisis, looking for the future of nursing science throughout the principle of Kuhn’s paradigm shift might provide valuable direction for nurse. Therefore, this paper aims to explain Kuhn’s revolutionary theory and its principle. Moreover, the influence of Kuhn’s revolution theory on the revolution of nursing science that will be present in this paper.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".