An Evaluation of Racial and Ethnic Representation in Research Conducted with Young Adults Diagnosed with Cancer: Challenges and Considerations for Building More Equitable and Inclusive Research Practices
Bibliographic record
Abstract
The psychosocial outcomes of adolescents and young adults (AYAs) diagnosed with cancer are poorer compared to their peers without cancer. However, AYAs with cancer from diverse racial and ethnic groups have been under-represented in research, which contributes to an incomplete understanding of the psychosocial outcomes of all AYAs with cancer. This paper evaluated the racial and ethnic representation in research on AYAs diagnosed with cancer using observational, cross-sectional data from the large Young Adults with Cancer in Their Prime (YACPRIME) study. The purpose was to better understand the psychosocial outcomes for those from diverse racial and ethnic groups. A total of 622 participants with a mean age of 34.15 years completed an online survey, including measures of post-traumatic growth, quality of life, psychological distress, and social support. Of this sample, 2% (n = 13) of the participants self-identified as Indigenous, 3% (n = 21) as Asian, 3% (n = 20) as “other,” 4% (n = 25) as multi-racial, and 87% (n = 543) as White. A one-way ANOVA indicated a statistically significant difference between racial and ethnic groups in relation to spiritual change, a subscale of post-traumatic growth, F(4,548) = 6.02, p < 0.001. Post hoc analyses showed that those under the “other” category endorsed greater levels of spiritual change than those who identified as multi-racial (p < 0.001, 95% CI = [2.49,7.09]) and those who identified as White (p < 0.001, 95% CI = [1.60,5.04]). Similarly, participants that identified as Indigenous endorsed greater levels of spiritual change than those that identified as White (p = 0.03, 95% CI = [1.16,4.08]) and those that identified as multi-racial (p = 0.005, 95% CI = [1.10,6.07]). We provided an extensive discussion on the challenges and limitations of interpreting these findings, given the unequal and small sample sizes across groups. We concluded by outlining key recommendations for researchers to move towards greater equity, inclusivity, and culturally responsiveness in future work.
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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 | 0.688 | 0.635 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".