Mapping gender and sexual minority representation in cancer research: a scoping review protocol
Bibliographic record
Abstract
BACKGROUND: Addressing the risk of people from gender and sexual minority (GSM) groups experiencing inequities throughout the cancer continuum requires a robust evidence base. In this scoping review, we aim to map the literature on cancer outcomes among adults from GSM groups and the factors that influence them along the cancer continuum. METHODS: This mixed-methods scoping review will follow the approach outlined by JBI. We will systematically search electronic databases for literature in collaboration with a health sciences librarian. Two reviewers will screen titles and abstracts to determine eligibility based on inclusion criteria, and then retrieve full text articles for data extraction. Results will be reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. Quantitative data will be qualitized through a narrative interpretation and pooled with qualitative data. We will use meta-aggregation to synthesize findings. This protocol was developed in collaboration with GSM patient and public advisors. We will engage people from GSM groups, community organizations and knowledge users in disseminating results. INTERPRETATION: This review will direct future research efforts by expanding the wider body of research examining cancer disparities across the cancer continuum that GSM groups experience, identifying literature gaps and limitations, and highlighting relevant social determinants of health that influence cancer outcomes for adults from GSM groups.
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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 | 0.178 | 0.162 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.069 | 0.017 |
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".