The Evolution of Data-Information-Knowledge-Wisdom in Nursing Informatics
Bibliographic record
Abstract
In Brief The data-information-knowledge-wisdom (DIKW) model has been widely adopted in nursing informatics. In this article, we examine the evolution of DIKW in nursing informatics while incorporating critiques from other disciplines. This includes examination of assumptions of linearity and hierarchy and an exploration of the implicit philosophical grounding of the model. Two guiding questions are considered: (1) Does DIKW serve clinical information systems, nurses, or both? and (2) What level of theory does DIKW occupy? The DIKW model has been valuable in advancing the independent field of nursing informatics. We offer that if the model is to continue to move forward, its role and functions must be explicitly addressed. This article presents an examination of assumptions of linearity and hierarchy, and an exploration of the implicit philosophical grounding of the data-information-knowledge-wisdom (DIKW) model. Two guiding questions are considered: 1) Does DIKW serve clinical information systems, nurses, or both? and 2) What level of theory does DIKW occupy? The DIKW model has been valuable in advancing the independent field of nursing informatics. We offer that if the model is to continue to move forward, its role and functions must be explicitly addressed.
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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 | 0.028 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.061 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".