Increasing participation of underrepresented groups in cancer early detection research: a scoping review
Bibliographic record
Abstract
Background: Improvements in access to early-detection research on cancer are still urgently needed to ensure that new research on early-stage cancer detection benefits all groups in society. To achieve this, cancer early detection (ED) studies must include participants from all walks of life. There are unique aspects to cancer early detection research that may deter potential research participants and complicate efforts to involve people from underrepresented backgrounds that require a review on its own merit. For instance, a unique risk for cancer ED research is overdiagnosis and overtreatment, in which a tumor is uncovered and treated that would not have led to the patient's death if left undiscovered and untreated. This potential 'side effect' of cancer ED research participation is particularly problematic for those without adequate access to healthcare and insurance. Methods: We conducted a targeted scoping review to identify empirically tested approaches to improve participation of underrepresented groups in cancer early detection research. Searches were conducted in PubMed and PsycINFO using terms related to cancer, research participation, and underserved populations in the title and/or abstract. Eligible studies were peer-reviewed, published between 2002 and April 2022, conducted in high-income countries, focused on adults without a cancer diagnosis, and reported on their evaluation of an intervention designed to improve recruitment or participation of minoritized groups in cancer early detection research. Data were extracted on study characteristics, barriers to participation, intervention strategies, and outcomes of the recruitment and engagement intervention that was assessed. We analyzed data extracted using narrative synthesis to identify cross-cutting themes across barriers to participating, and recruitment or engagement approaches. Results: This review identified themes in the 38 included studies that aimed to recruit and involve participants from underserved groups in cancer ED research so that future studies may learn from or further test these varied strategies. We narratively grouped the review in terms of the barriers identified, and the approaches that have been designed to improve participation. These included rethinking recruitment locations and partnerships with local communities, designing educational interventions, combining research with community needs, increasing cultural competence of research teams, and overcoming practical barriers in study design. Conclusion: This scoping literature review highlights various tools, empirically tested, that research teams can employ to improve participation rates of groups underrepresented in cancer ED research. Combinations of these methods could help overcome the perceived barriers to participation in cancer research that mainly affect people without a cancer diagnosis from these minoritized groups. Not only would these methods increase the generalizability and representativeness of studies; the highlighted approaches also contribute to a more significant shift in research culture toward less extractive and more trusting relationships between researchers and the public.
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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 | 0.025 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".