Factors Affecting the Cervical Cancer Screening Behaviors of Japanese Women in Their 20s and 30s Using a Health Belief Model: A Cross-Sectional Study
Bibliographic record
Abstract
In recent years, the incidence and mortality rates of cervical cancer (CC) have increased among young women. Cervical cancer screening (CCS) is crucial to reducing the incidence and mortality of CC in a country such as Japan, where it is challenging to raise HPV vaccination rates. The purpose of this study was to identify psychological and personal characteristics relating to CCS participation among young people by using the Health Belief Model (HBM). For this cross-sectional study, an internet survey was conducted between February-March 2018. Based on HBM and personal characteristics, χ2 tests and logistic analyses were used to identify factors influencing CCS. Responses obtained from 816 women in their 20s and 30s were used in the analysis. For HBM-based psychological characteristics, the odds ratios were significantly higher for "cues to participation in screening" and "barriers to participation at the time of cancer screening", while "barriers to participation before cancer screening" showed significantly lower odds ratios. On the other hand, it was found that the presence of children and having regular health checkups affected the attributes of screening that were significant for decision-making. Therefore, it is important to create proactive measures to encourage younger women to undergo medical examinations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".