Building a Pan-Canadian Primary Care Sentinel Surveillance Network: Initial Development and Moving Forward
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The development of a pan-Canadian network of primary care research networks for studying issues in primary care has been the vision of Canadian primary care researchers for many years. With the opportunity for funding from the Public Health Agency of Canada and the support of the College of Family Physicians of Canada, we have planned and developed a project to assess the feasibility of a network of networks of family medicine practices that exclusively use electronic medical records. The Canadian Primary Care Sentinel Surveillance Network will collect longitudinal data from practices across Canada to assess the primary care epidemiology and management of 5 chronic diseases: hypertension, diabetes, depression, chronic obstructive lung disease, and osteoarthritis. This article reports on the 7-month first phase of the feasibility project of 7 regional networks in Canada to develop a business plan, including governance, mission, and vision; develop memorandum of agreements with the regional networks and their respective universities; develop and obtain approval of research ethics board applications; develop methods for data extraction, a Canadian Primary Care Sentinel Surveillance Network database, and initial assessment of the types of data that can be extracted; and recruitment of 10 practices at each network that use electronic medical records. The project will continue in phase 2 of the feasibility testing until April 2010.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it