Bibliographic record
Abstract
Data quality requirements are increasing as a wider range of data becomes available and the technology to mine data shows the value of data that is ‘fit for use’. This chapter describes a data quality programme for the New Zealand Ministry of Health that first isolates the criteria that define ‘fitness’ and then develops a framework as the basis of a health sector wide data quality strategy that aligns with the sector’s existing strategies and policies for the use of health information.. The framework development builds on an existing work by the Canadian Institute for Health Information, and takes into account current data quality literature and recognised Total Data Quality Management (TDQM) principles. Strategy development builds upon existing policy and strategy within the New Zealand health sector, a review of customer requirements, current sector maturity and adaptability, and current literature to provide a practical strategy that offers clear guidelines for action. The chapter ends with a summary of key issues that can be employed by health care organisations to develop their own successful data quality improvement programmes
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".