Correlation between Hospital Management Factors with Cancer Death Rates: An Example from G7 Countries
Bibliographic record
Abstract
Abstract Objectives Cancer, which is characterized by the abnormal, uncontrolled growth and spread of cells in the body, is one of the most imminent diseases of our era. Cancer can occur anywhere, regardless of geographic boundaries, and it is a problem and health issue both for developed and developing countries that any person from any country in any age group may be affected. The main aim is to identify the relation between the cancer death rates and hospital management over two decades.Methods Within the scope of this study, the correlation between the cancer death rates in G7 countries (Germany, United States, United Kingdom, Italy, France, Japan and Canada) and nurse, doctor, hospital stay, hospital discharge and hospital bed rates were statistically analysed for the period between 2000 and 2020.Results High levels of health in developed countries and associated managerial improvement of hospitals reduce the cancer death rates. The study findings were interpreted through Eviews statistics software, which is developed by the company called Quantitative Micro Software (QMS). Eviews is a very common program for econometric analysis allowing performance of time series, panel data and horizontal sectional data analysis. For this study, this analytical tool was used due to its time series generation feature for data breakdown. For the analysis of sufficient number of studies and estimation of any unknown regression parameter, the test panel was tested with pedroni cointegration in addition to the ordinary least squares test.Conclusions The test results concluded a long term (20-year) correlation between the variables.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".