A Scoping Review on Barriers to Cancer Diagnosis and Care in Low- and Middle-Income Countries
Bibliographic record
Abstract
Cancer remains a significant global health challenge, with low- and middle-income countries (LMIC) disproportionately burdened by high mortality rates despite a lower overall incidence. Barriers to timely diagnosis and care exacerbate these disparities. This scoping review synthesizes existing literature on barriers for women in LMICs following the Joanna Briggs Institute methodology and the Preferred Reporting Items for Systematic Review and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines. Studies on women in LMICs reporting barriers to accessing care for breast, colorectal, lung, cervix uteri, thyroid, corpus uteri, and stomach cancers were included. Twenty-nine studies involving 7,031 participants were included. The most common barriers included financial challenges (65.5%), geographic obstacles (34.5%), health system limitations (55.2%), and low health literacy (51.7%). Patients experienced significant delays, averaging 7.4 months from symptom onset to diagnosis and 4.9 months from diagnosis to treatment initiation. Structural issues such as limited diagnostic services, inadequate healthcare infrastructure, and healthcare provider shortages were widespread. Addressing the multifaceted barriers to cancer care in LMICs requires comprehensive strategies, including increasing financial support, decentralizing care services, improving healthcare infrastructure, and enhancing education for patients and providers. Policymakers and stakeholders should prioritize investments in cancer care to reduce disparities and improve outcomes. These findings will inform strategies for improving cancer care in low-resource settings globally.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.026 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".