A novel in-service nursing education optimizing theory of technological competency as caring in nursing
Bibliographic record
Abstract
Contemporary and future nursing practices are increasingly being designed with nursing theories as to its foundation. The aim of this article is to describe an in-service education program for nursing administrators centered on the theory of Technological Competency as Caring in Nursing (TCCN). This theory is framed chiefly within the concepts of technology, caring, nursing, and technological competency. Influencing the significance of in-service education is theory-based practice with advancing technologies in human caring. The in-service education program was organized as a five-month, one-hour a month lecture and discussion series. In each session, educational contents are focused on the nursing process as caring based on the theory of TCCN. This education is a plan that will gradually educate the nurse manager group, the mid-level nursing staff group, and finally to the staff nurse group. This hierarchically organized in-service educational plan aims to systematically improve their knowledge and practice situation for three years. During each session, theory content included “knowing persons as caring” as the nursing process based on the theory of TCCN. Participating in these lectures are envisioned to increase knowledge about TCCN for the purpose of improving the overall quality of nursing care outcomes. An organized educational plan will improve the quality of nursing care as influenced by the use of the theory of TCCN in the practice of nursing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".