Gender inequalities across ethnicities in primary care cancer referrals: a scoping review protocol
Bibliographic record
Abstract
BACKGROUND: Early cancer diagnosis is associated with improved mortality and morbidity; however, studies indicate that women and individuals from ethnic minorities experience longer times to diagnosis and worse prognosis compared with their counterparts for various cancers. In countries with a gatekeeper healthcare system, such as the UK, most suspected cancer referrals are initiated in primary care. AIM: To understand the extent of evidence available on the relationship between primary care cancer referral pathways and cancer outcomes in relation to gender across different ethnic groups. This will identify research gaps and enable development of strategies to ease potential inequalities in cancer diagnosis. DESIGN & SETTING: A scoping review of articles written in English, based on the Joanna Briggs Institute methodology. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) will be used. METHOD: Electronic databases and private collections of the team members will be searched for studies. Two independent reviewers will carry out the study selection and data extraction. Based on Population (or participants), Concept, and Context (PCC) framework, this review will consider studies after year 2000, which explored the relationship between gender, across various ethnic groups, and cancer outcomes, following primary care cancer referral in countries with gatekeeper healthcare systems (UK, New Zealand, Sweden, Australia, Canada, Denmark, Republic of Ireland, and Norway). Results will be presented as a narrative analysis. CONCLUSION: The results are expected to provide an overview of the discrepancies in primary care cancer referrals based on gender across ethnic groups, which will be crucial to define an appropriate range of strategies to ease any inequalities in primary healthcare cancer diagnosis.
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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 | 0.111 | 0.098 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.068 | 0.012 |
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".