Capturing Pan-Canadian Primary Health Care Indicator Data Using Multiple Approaches for Data Collection
Bibliographic record
Abstract
The Canadian Institute for Health Information (CIHI), in collaboration with diverse stakeholders, led the development of pan-Canadian indicators to measure primary health care. In 2006, CIHI released a set of 105 pan-Canadian Primary Health Care (PHC) indicators that were developed with the assistance of national, provincial and territorial representatives, clinicians and researchers. Additionally, data gaps were identified in a series of reports. In 2006 and 2007, CIHI assessed options for closing the data gaps so that the indicators could be measured and reported. CIHI then began a program to build the data infrastructure needed for the PHC indicators. The program included the development of content standards for electronic medical records, a prototype of a voluntary reporting system, enhancements to surveys, and the development of reports. In 2006, fewer than 10% of the 105 indicators could be calculated with existing data sources. Now, four projects have begun and over 50% of the indicators are being captured. Important relationships have been established with key collaborators. These relationships will lead to the development of a reporting system prototype and to the refinement of PHC indicators and electronic medical record (EMR) content standards. The project for pan-Canadian PHC indicators has encouraged consultation and synergy. It has motivated CIHI to establish an information program to fill data gaps and to make PHC indicators available.
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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 | 0.087 | 0.145 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.024 | 0.055 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".