Modeling healthcare quality: life expectancy SURS in the G7 countries and Korea
Bibliographic record
Abstract
In this study I have made efforts towards investigating healthcare in two arenas. First, can a model with life expectancy as a proxy for healthcare quality be used to objectify the study of efficiency in the G7 countries and Korea? Table 1 and the results section have illuminated many factor variables which vary between countries and characterize the environments in which different healthcare systems have developed. The analysis also illuminates an inherent structural difference in the mechanism of delivering healthcare throughout the developed world. Secondly, can these aggregate data be used to show us anything new about the studies performed by Peter Zweifel and Friedrich Breyer? Did the SISYPHUS Syndrome disappear in the early 1990s as Zweifel suggested in 2002? No, in Table 2 I have demonstrated through SURS that over the time period 1990-2009 there are clear statistically significant SISYPH variables in at least Canada, Germany, Korea, and Britain. Lastly, can I confirm Breyer’s model of HCE in Germany and can it be useful in other countries? Yes to extent possible the methodologies were replicated in a SURS fashion in an effort to simultaneously test and examine different variables in different countries. I was unable to confirm the results of Breyer in his 2011 examination of the sickness fund members for Germany. However, I was able to offer primitive characterizations of the other G7 countries and Korea and how their HCE move.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".