The Development of a Data Quality Framework and Strategy for the New Zealand Ministry of Health
Bibliographic record
Abstract
This research in progress aims to develop a data quality strategy for the Ministry of Health. Data quality requirements are increasing as a wider range of data becomes available and the technology to mine the data shows the value of data that is ‘fit for use’. A data quality framework is the initial step towards an organisation wide data quality strategy that aligns with the sectors existing strategies and policies. The framework development builds on an existing framework developed by the Canadian Institute for Health Information, and takes into account current data quality literature and recognised Total Quality Management Principles. Further piloting of the framework through assessment of national health collections will provide the information on which to base an effective data quality strategy for the Ministry of Health. 1.
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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 | 0.304 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.027 | 0.020 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".