Barriers to cancer screening uptake and approaches to overcome them: a systematic literature review
Bibliographic record
Abstract
Introduction: Cancer screening programs play a crucial role in early detection, improving survival rates and reducing the burden of advanced cancer. However, uptake remains inconsistent due to multifaceted barriers. This systematic review aimed to identify factors that impact cancer screening uptake across multiple tumor types and interventions to overcome barriers to cancer screening uptake. Methods: (May 2012 to May 2022) to identify observational studies that reported factors associated with screening uptake in adults, worldwide, with no tumor-specific restrictions. Records identified were screened by two independent reviewers. Included studies were data extracted by two reviewers and the results were reported narratively, focusing on identifying factors that acted as barriers or facilitators to cancer screening uptake, along with potential interventions to improve screening uptake. Results: Overall, 811 studies were identified from the SLR that reported factors influencing the uptake of screening programs, with 658 studies covering screening programs for breast, cervical, lung, colorectal, gastric and prostate cancers. Barriers to cancer screening included: being unmarried, experiencing higher deprivation, lower socioeconomic status and rural living conditions. Facilitators to cancer screening included: older age, poor perception of health, previous cancer history, family history of cancer, previous cancer screening history, having knowledge of the disease, positive attitudes to screening, perceived cancer risk, higher education level, having children, higher income, higher socioeconomic status, having health insurance, urban residence, having access to care, and recommendations for screening by primary care physicians. Mixed findings were identified for race and ethnicity, employment and smoking status. Targeted educational programs were the most suggested strategy to overcome barriers to cancer screening uptake. Conclusion: Barriers to cancer screening across multiple tumor types are complex, spanning demographic and patient-level factors, social and economic factors, provider and community challenges, and access to health care. While certain barriers are shared across tumor types, others are unique, reflecting the specific requirements of screening for different tumors. Addressing these barriers requires multi-level strategies that integrate both universal and cancer-specific approaches. Targeted interventions and supportive policies can increase screening participation, facilitate earlier cancer diagnosis, and reduce disparities in cancer outcomes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".