Understanding the Intersection of Identity and Cancer Experience Among Racially, Ethnically, Gender and Sexual Minoritized Adolescents and Young Adults With Cancer
Bibliographic record
Abstract
OBJECTIVES: Adolescents and young adults (AYAs, 18-39 years) with cancer identifying as racially/ethnically minoritized or 2SLGBTQIA+ (Two-Spirit, lesbian, gay, bisexual, transgender, queer, intersex, asexual and "+" referring to other queer identities) have been underrepresented in cancer research. This study explores the aspects of identity that hold significance for these minoritized AYAs and how these facets impact their healthcare experiences. METHODS: Eligible participants comprised English-speaking AYAs who self-identified as racially/ethnically minoritized and/or 2SLGBTQIA+, were diagnosed with cancer between the ages of 15-39, currently aged > 18, and had received or were receiving cancer care within Canadian healthcare system. Additionally, four patient partners meeting the same criteria were recruited as research collaborators. Semi-structured one-on-one virtual interviews guided by an interview script were conducted, and qualitative analysis employed a framework approach. RESULTS: We recruited 23 participants from 4 Canadian provinces (mean age: 28, Range: 20-44); 17 identified as racially/ethnically minoritized, one as sexual/gender minoritized, and five as racially/ethnically and sexually/gender minoritized. Participants emphasized that their culture/ethnicity, religion/spirituality, sexuality, gender, family, career, and being an immigrant are important aspects of their identity, with only one participant recognizing their identity as a "person with cancer". A cancer diagnosis altered the aspects of identity deemed most significant by participants. Both visible and invisible aspects of identity shaped participants' experiences and influenced their level of trust in the healthcare system. CONCLUSION: Racially, ethnically, gender, or sexually minoritized AYAs with cancer place considerable importance on aspects of their identity that are shaped by their respective communities. Recognizing and respecting these identities are paramount for healthcare professionals to deliver safe and inclusive care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".