Interventions to improve access to cancer care in underserved populations in high income countries: a systematic review
Bibliographic record
Abstract
Background: Underserved populations both globally and in Canada face serious cancer inequities that result from systemic economic, environmental, and social conditions. These pose barriers in access to cancer care and lead to suboptimal cancer care experiences and outcomes. Knowledge of effective interventions to improve access to cancer care is needed to inform the design of tailored interventions for these populations. Aim: To identify interventions and programs to improve access to cancer care for underserved populations in high income countries with universal health coverage (UHC) and the United States (US) throughout the cancer care continuum. Methods: We conducted a systematic review following the PRISMA standards. We searched Medline, EMBASE, PsycINFO, CINAHL, Scopus, and the Cochrane Library. Inclusion criteria: quantitative and qualitative studies published in English in the last 10 years (2013-2023), describing interventions/programs to improve access to cancer care for underserved populations (18 years and over). We included studies in the US given the body of scholarship on equity in cancer care in that country. Screening, data extraction and analysis were undertaken by two independent reviewers. Results: Our search yielded 7,549 articles, and 74 met the inclusion criteria. Of these, 56 were conducted in the US, 8 in Australia, 6 in Canada, and 4 in the United Kingdom. Most (90.5%) were quantitative studies and 47.3% were published between 2020-2023. Seven types of interventions were identified: patient navigation, education and counselling, virtual health, service redesign, financial support, improving geographical accessibility and multicomponent interventions. Interventions were mainly designed to mitigate language, distance, financial, lack of knowledge and cultural barriers. Most interventions focused on access to cancer screening, targeted rural populations, racialized groups and people with low socioeconomic status, and were conducted in community-based settings. The majority of interventions or programs significantly improved access to cancer care. Conclusion: Our systematic review findings suggest that interventions designed to remove specific barriers faced by underserved populations can improve access to cancer care. Few studies came from countries with UHC. Research is required to understand tailored interventions for underserved populations in countries with UHC.
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".