Assessment of the Nursing Quality Indicators for Reporting and Evaluation (NQuIRE) database using a data quality index
Bibliographic record
Abstract
A comprehensive data quality assessment is necessary to expand a nursing database that is designed for evaluating the impact of implementing Best Practice Guidelines (BPG) developed by the Registered Nurses' Association of Ontario (RNAO). This case report presents a method to standardize data quality assessments of the Nursing Quality Indicators for Reporting and Evaluation (NQuIRE) database by developing a data quality framework (DQF) and assessing key dimensions of the framework using a data quality index (DQI). The data quality index is a single key performance metric for assessing the quality of the database. The aims of sharing this case report are 2-fold: (1) to promote best practices for assessing data quality by developing and implementing a data quality framework and (2) to demonstrate an unprecedented method of assessing the data quality of a nursing database.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.145 | 0.260 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".