Bibliographic record
Abstract
Background : Cancer is a disease that not only places a significant burden on clinically but also requires significant expense for diagnosis and treatment. Although the cancer coverage of health insurance has recently been expended, the need for financial assistance among cancer and their families is still expected to be significant. In this study, cancer patients need for financial assistance in Korea was examined and its influence factors were analyzed. Methods : Target study subjects were those who are over 18 years of age and were diagnosed with cancer more than four months prior at the National Cancer Center and 9 Regional Cancer Centers in Korea during the period from July to August of 2008. Quarter sampling was conducted according to the ratio of the type of each cancer. A face to face interview survey was conducted. A total of 2,661 cancer finished the survey. Medical charts were reviewed in order to obtain the cancer type and SEER stage of cancer patients. An ordered logistic regression model was used to examine the level of need for financial assistance according to the demographical, clinical, and socio-economic variables of cancer patients. Result : The percentage of cancer who needed financial assistance was 69.0%, and 36.9% needed significant financial assistance. The need for financial assistance was perceived to be greater in males, younger age group, low income group, low education group, medical aid recipients, those who were diagnosed recently, those with a low level of quality of life measured through EQ5D, and those with decreased income after cancer diagnosis. Conclusion : In spite of the current policy to increase health insurance coverage, the majority of cancer and their families in Korea still need financial assistance due to cancer. In particular, there were more vulnerable groups, such as the low income, or low education group. In the future, policies that focus on the disadvantaged, which strengthen social security, should be considered for achievement of a substantially better quality of life for cancer and their families.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.052 | 0.035 |
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; both teacher heads agree on what is shown here.
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".