Developing a Tool to Assess the Quality of Socio-Demographic Data in Community Health Centres
Bibliographic record
Abstract
OBJECTIVE: The objectives of this study are to 1) create a quality assessment tool for socio-demographic data aligned with the needs of Community Health Centres (CHCs) and based on the data quality framework of the Canadian Institute for Health Information (CIHI), and 2) test the feasibility of the tool in CHCs. METHODS: The tool was developed based on both theoretical and practical knowledge. A review of the literature was performed to identify data quality frameworks and dimensions that could be employed. In addition, informal discussions with Community Health Centres staff members holding various positions were conducted and a team of subject matter experts was established. This approach supported the alignment between the tool (i.e., the indicators developed, the rating scale, and weighting system) and the setting for which it has been designed. The tool was pilot tested in five CHCs across Ontario. RESULTS: The decision to focus on socio-demographic data was based on findings from the discussions with staff members. The team established nine principles for the development of the tool, including the use of computer software, whenever possible, to query the data and ensure consistency of the measurement. Data quality scores ranged from 45 to 74 on a scale of 0 (lowest quality) to 100 (highest data quality), with one CHC that was not able to run all of the queries. The feedback from staff was positive and supports the feasibility of the tool as an application of the CIHI data quality framework in a local setting. CONCLUSION: Pilot test results demonstrate the feasibility of the tool and an applicability of the CIHI framework as a basis for developing tools for data quality assessment in health care organizations.
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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 | 0.142 | 0.286 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.022 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".