Characterizing the Gilbert-Elliott Parameter Space under LDPC Decoding
Bibliographic record
Abstract
In 2002, Turkey started to implement reforms in health care aiming to improve access and increase efficiency. Reforms increased health insurance coverage and resulted in higher number of outpatient and inpatient treatments at both public and private hospitals. Later, to change preference towards the use of secondary and tertiary care over primary care and rein in increasing health expenditures, a series of co-payments were instituted along with an extension of primary care services through a family-medicine system that provided free access to all. This work aims to measure the impact of these two simultaneous policy measures on out-of-pocket expenditures. We find that while contributory payments resulted in higher OOP health expenditures, especially for lower income households, the impact was small. We also observe that inability to consult a physician and to visit a hospital, especially for monetary reasons, was reduced after the policy change.
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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 | 0.002 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".