Data Mining as a Tool for Research and Knowledge Development in Nursing
Bibliographic record
Abstract
The ability to collect and store data has grown at a dramatic rate in all disciplines over the past two decades. Healthcare has been no exception. The shift toward evidence-based practice and outcomes research presents significant opportunities and challenges to extract meaningful information from massive amounts of clinical data to transform it into the best available knowledge to guide nursing practice. Data mining, a step in the process of Knowledge Discovery in Databases, is a method of unearthing information from large data sets. Built upon statistical analysis, artificial intelligence, and machine learning technologies, data mining can analyze massive amounts of data and provide useful and interesting information about patterns and relationships that exist within the data that might otherwise be missed. As domain experts, nurse researchers are in ideal positions to use this proven technology to transform the information that is available in existing data repositories into useful and understandable knowledge to guide nursing practice and for active interdisciplinary collaboration and research.
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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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".