Women’s preferences and experiences of cervical cancer screening in rural and remote areas: a systematic review and qualitative meta-synthesis
Bibliographic record
Abstract
INTRODUCTION: Cervical cancer is one of the leading causes of mortality in women. Population-based cervical cancer screening programs have been highly effective in reducing the incidence and mortality of cervical cancer worldwide. However, disparities remain in women's cervical cancer screening participation rates, especially in rural and remote areas, where access to health care may be circumscribed due to logistical barriers. Until now, there has been no effort to review and synthesize the perspectives and experiences of women accessing cervical cancer screening in rural and remote areas. This systematic review and qualitative meta-synthesis of 14 studies aimed to describe and elaborate the issues women face when accessing cervical cancer screening in rural and remote areas. METHODS: This study used the qualitative meta-synthesis approach to review 14 studies on rural women's participation in cervical cancer screening. This research approach synthesized findings from multiple, primary qualitative studies to produce a new interpretation of the phenomenon while retaining the original meaning of each qualitative study. RESULTS: After 4937 citations were screened by database searching, 117 were retrieved for full-text review, of which 14 studies were included. This study identified two themes that modulate rural women's access to cervical cancer screening: interactions with healthcare providers and healthcare system access. Furthermore, this study found that women frequently expressed issues around patient-centered care in their interactions with healthcare providers. The implications of these findings for program design and delivery efforts in rural and remote areas are discussed. CONCLUSION: This article provides the foundation for tailoring interventions and programming to increase cervical cancer screening rates in women who reside in rural and remote areas. This review also clarifies the factors of patient-centered care that may be adopted to enhance the quality of care for women in rural and remote areas. In summary, this systematic review and qualitative meta-synthesis provide information about women's perspectives and experiences accessing cervical cancer screening in rural and remote areas. The review has strong implications for this population and can be used to inform future research and program design initiatives.
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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 | 0.058 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".