Demographic and socioeconomic factors associated with cervical cancer screening among women in Serbia
Bibliographic record
Abstract
Objectives: Effective reduction of cervical cancer incidence and mortality requires strategic measures encompassing the implementation of a cost-effective screening technology. Serbia has made significant strides, introducing organized cervical cancer screening in 2012. However, various impediments to screening implementation persist. The aim of the study was to estimate the socioeconomic factors associated with cervical cancer screening among women in Serbia. Methods: Data from 2019 National Health Survey of the population of Serbia were used in this study. The study is cross sectional survey on a representative sample of the population of Serbia. Present total number of participants analyzed in survey 6,747. Results: In Serbia, 67.2% of women have done a Pap test at any time during their lives, of which 46.1% of women have undergone cervical cancer screening in the past 3 years. About a quarter of women have never undergone a Pap test in their life (24.3%). The probability of never having a Pap test have: the youngest age group (15-24 years) is 1.3 times more likely than the oldest age group (OR = 1.31), unmarried women 0.3 times more often than married women (OR = 0.37), respondents with basic education 0.9 times more often than married women (OR = 0.98), the women of lower socioeconomic status 0.5 times more often than respondents of high socioeconomic status (OR = 0.56). Conclusion: Enhancement of the existing CCS would be the appropriate public health approach to decrease the incidence and mortality of cervical cancer in the Republic of Serbia.
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 | 0.000 | 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 | 0.000 | 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) | 0.002 | 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".