Inequalities in cancer screening, prevention and service engagement between UK ethnic minority groups
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
More people in the UK are living with cancer than ever before. With an increasingly ethnically diverse population, greater emphasis must be placed on understanding factors influencing cancer outcomes. This review seeks to explore UK-specific variations in engagement with cancer services in minority ethnic groups and describe successful interventions. The authors wish to highlight that, despite improvement to engagement and education strategies, inequalities still persist and work to improve cancer outcomes across our communities still needs to be prioritised. There are many reasons why cancer healthcare inequities exist for minority communities, reported on a spectrum ranging from cultural beliefs and awareness, through to racism. Strategies that successfully enhanced engagement included language support; culturally-sensitive reminders; community-based health workers and targeted outreach. Focusing on the diverse city of Leicester the authors describe how healthcare providers, researchers and community champions have worked collectively, delivering targeted community-based strategies to improve awareness and access to cancer services.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it