Knowledge on cancer education and prevention and its use in the context of tobacco smoking in Poland
Bibliographic record
Abstract
Introduction Cancer is the second most common cause of death in developed countries. Lung cancer is the most frequent cancer in the world and it occurs almost exclusively in smokers and people exposed to secondhand smoke. Evidence of the association between tobacco smoke and cancer appeared in the first half of the 20th century. In 1986, the International Agency for Research on Cancer (IARC) announced that smoking causes not only lung cancer but also respiratory, pancreatic and lower urinary tract cancers. In 2004, the official list of diseases caused by tobacco contained 14 different cancers. It is clear from this that tobacco smoke affects almost every internal organ. Methods The data comes from the Awareness of Cancer and Prevention survey constructed as part of the National Program for Cancer Prevention by the Ministry of Health and conducted in 2014. Results 86.5% of Poles reckon that everyone should take care of their own health and 84.4% of them believe they do so. 93% of respondents heard that smoking causes lung cancer and 96.9% of people think that giving up smoking can protect against lung cancer. 29% of people with primary, 36.5% with basic vocational, 27.7% with secondary, and 18.9% of people with higher education do not apply to knowledge and declare that they smoke. Conclusions Most Poles believe that they care about their own health, but declarations often differ from the actual state. Our knowledge does not always translate into behavior. The government should educate, train and implement preventive programs, thanks to which preventive actions will be associated not only with medical examinations but also with a healthy lifestyle. Preventive programs should be addressed especially to environments with a lower level of education.
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 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".