Data Quality in the Ontario Midwifery Program Database, 2006 to 2009
Bibliographic record
Abstract
Objective: To identify common errors in midwifery data collection and provide midwives with the rationales behind data cleaning, the importance of reliable data, and the links between data collection, research studies, and evidence-based care. Methods: A database containing records of all women who received midwifery care in Ontario that was invoiced to the Ministry of Health and Long-Term Care between April 1, 2006, and March 31, 2009, was obtained. Data cleaning was performed to assure that the data set was as complete and accurate as possible. Duplicate records were identified and removed. Missing, inconsistent, and implausible data were identified and corrected where possible or removed. Results: Common data errors included inappropriate use of open text fields and drop-down menus, incorrect interpretation of “planned place of birth,” reporting of outcomes that should be mutually exclusive, and reporting of incorrect, incomplete, or missing information. Discussion: Midwives have an important role in the collection of health information that is complete and accurate. Several common errors were identified that, if corrected, would improve the quality of midwifery data and in turn would contribute to high‐quality research, which will inform midwifery practice, policy‐makers, and women and their families about midwifery care. This article has been peer reviewed.
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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 | 0.014 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.023 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".