Knowledge Creation in Nursing Education
Bibliographic record
Abstract
In today's society, knowledge is recognized as a valuable social asset and the educational system is in search of a new strategy that allows them to construct their knowledge and experience. The purpose of this study was to explore the process of knowledge creation in nursing education. In the present study, the grounded theory approach was used. This method provides a comprehensive approach to collecting, organizing, and analyzing data. Data were obtained through 17 semi-structured interviews with nursing faculties and nursing students. Purposeful and theoretical sampling was conducted. Based on the method of Strauss and Corbin, the data were analyzed using fragmented, deep, and constant-comparative methods. The main categories included striving for growth and reduction of ambiguity, use of knowledge resources, dynamism of mind and social factors, converting knowledge, and creating knowledge. Knowledge was converted through mind processes, individual and group reflection, praxis and research, and resulted in the creation of nursing knowledge. Discrete nursing knowledge is gained through disconformity research in order to gain more individual advantages. The consequence of this analysis was gaining new knowledge. Knowledge management must be included in the mission and strategic planning of nursing education, and it should be planned through operational planning in order to create applicable knowledge.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".