Access to cancer clinical trials for racialised older adults: an equity-focused rapid scoping review protocol
Bibliographic record
Abstract
BACKGROUND: The intersection of race and older age compounds existing health disparities experienced by historically marginalised communities. Therefore, racialised older adults with cancer are more disadvantaged in their access to cancer clinical trials compared with age-matched counterparts. To determine what has already been published in this area, the rapid scoping review question are: what are the barriers, facilitators and potential solutions for enhancing access to cancer clinical trials among racialised older adults? METHODS: We will use a rapid scoping review methodology in which we follow the six-step framework of Arksey and O'Malley, including a systematic search of the literature with abstract and full-text screening to be conducted by two independent reviewers, data abstraction by one reviewer and verification by a second reviewer using an Excel data abstraction sheet. Articles focusing on persons aged 18 and over who identify as a racialised person with cancer, that describe therapies/therapeutic interventions/prevention/outcomes related to barriers, facilitators and solutions to enhancing access to and equity in cancer clinical trials will be eligible for inclusion in this rapid scoping review. ETHICS AND DISSEMINATION: All data will be extracted from published literature. Hence, ethical approval and patient informed consent are not required. The findings of the scoping review will be submitted for publication in a peer-reviewed journal and presentation at international conferences.
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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 | 0.168 | 0.145 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.086 | 0.023 |
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".