Barriers to Cervical Cancer Screening in HIV Positive women: A Systematic Review of Recent Studies in the World
Bibliographic record
Abstract
Background and Objectives: Cervical cancer is one of the five most common cancers in Iranian women. Considering the impact of HIV on cervical cancer and the low rate of cervical cancer screening in HIV positive women, this study was conducted to review the barriers to cervical cancer screening in HIV positive women. Methods: In this systematic review, data were retrieved from Magiran, SID, Irandoc, Prequest, OVID, ScienceDirect, PubMed, Web of Science and Scupos databases from January 2000 to January 2018. The following keywords and their combination were used: cervical cancer screening, Pap smear, HIV-positive women, and barriers. The NOS (Newcastle-Ottawa Scale) checklist was used to evaluate the quality of the selected articles and the articles that scored more than six were included in the study. Results: From 145 selected articles, 21 were included in the review based on the inclusion criteria. The most common reported screening barriers were the costs of test, lack of awareness, low education level, younger age, lack of information about screening centers, and fear of sampling. Conclusion: Considering the barriers to cervical cancer screening, increasing the level of awareness of the HIV-positive women, preparing free Pap smear services, and providing easier access to health centers for this high risk group could lead to early detection of cervical cancer.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".