A geocoded dataset of primary health care clinics in Brazil
Bibliographic record
Abstract
We develop a geocoded dataset of primary health care clinics in Brazil. We merge data from three publicly available sources. The first is the National Registry of Healthcare Facilities (CNES-ST), which collects the location (state, municipality, and 8-digit postal code) of all health care facilities, public or private, operating in Brazil. The second is the National Registry of Addresses for Statistical Purposes (IBGE-CNEFE), which contains the geographic coordinates of all addresses in Brazil (including 8-digit postal codes) and serves as the basis for the Brazilian census. Our approach aggregates individual (address-level) coordinates to the 8-digit postal code, and assigns coordinates to primary care clinics based on each clinics' postal code. Using data from a third source, the IBGE shapefiles, we estimate the area of postal codes to evaluate the precision of our geo-referencing method. The unique facility identification number (cnes number) can be used to merge our georeferenced data with other publicly available databases of the Brazilian Unified Health System. The final dataset is an unbalanced panel with monthly observations about 293,698 primary care clinics' locations (i.e. coordinates), from January 2018 to December 2023, totalling 15,455,219 observations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".