The role of socio-economic and scientometric indicators in the cancer mortality rate
Bibliographic record
Abstract
Scientific research in the field of healthcare contributes to solving not only medical, but also economic and social issues. One of the latest trends is the growing interest in evaluating the effectiveness of research conducted. In the current study, we have hypothesized that science contributes to the reduction of the Cancer Mortality Rate (CMR) by making awareness about and bringing attention to this disease. The purpose of our investigation is to study the possible correlation between five scientometric indicators (Web of Science Documents, International Collaborations, etc.) and CMR changes for 14 countries. Furthermore, the expenditures of GDP in both science and healthcare for each of the studied countries have been considered within the framework of cancer-science relations in order to find out the possible socio-economic impact on cancer incidence. Methodologically, the study relies on the principles of scientometric management. The research data were retrieved from Web of Science and the World Health Organization for the period from 1997 to 2017. To investigate the correlation between scientific research and the CMR, we have used bibliometric data and nonparametric statistical methods (the Kruskal-Wallis test, Spearman’s correlation coefficient) as well as the Dunn test of multiple group checks and the Shapiro-Wilk test. R language, Tidyverse package R and VOSviewer were used for data processing. The research results showed that during the period in question there was an increase in the CMR in Armenia and Georgia, while in Iran and Azerbaijan it remained almost consistent. For the rest of the countries from Asia and Europe, as well as Canada and the USA, the CMR experienced a downward trend. We have found close links between scientometric data, the CMR and economic costs for Europe and the USA. At the same time, for Armenia and neighbouring countries the correlation between the CMR and GDP was weak. Moreover, GDP costs incurred in healthcare and science did not have a positive effect on the CMR in Armenia, Azerbaijan and Georgia. This indicates that scientific and socio-economic factors are highly correlated with each other and, therefore, have a positive impact on the CMR, mainly in Europe and the USA. However, the science-health relationship in Armenia is still weak and requires efforts to prevent the continued rise in CMR levels. The findings of this study can also be applied to other fields of science and help to establish close links between scientometrics and various branches of medicine.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".