Applying an Extended Kernel Density 4-Step Floating Catchment Area Method to Identify Priority Districts to Promote New Publicly Financed Supply of Gastroenterology Exams
Bibliographic record
Abstract
In continental Portugal, the publicly financed supply of gastroenterology exams was limited since the end of the last century, restricted to a fixed set of private providers that was hired by the Portuguese state. This way of contracting created market entry barriers and is inefficient, since prices are administratively set. Besides, it produced access inequalities, because of the way that the supply was geographically distributed. This paper applies the Extended Kernel Density 4-Step Floating Catchment Area (EKD4SFCA) method to identify priority districts for the promotion of new supply by the state, in order to choose the appropriate way of contracting new private supply, as determined by current law, and to reduce access inequalities. The applied method enables the identification of the Portuguese regions with strong competition between health care providers and where patients’ access to publicly financed gastroenterology exams is relatively low. In these regions, the state should promote public bids to stimulate new supply, exploring thereby the potential for setting lower prices and reducing access inequalities.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".