Learnings from Racialized Adolescents and Young Adults with Lived Experiences of Cancer: “It’s Okay to Critique the System That Claims to Save Us”
Bibliographic record
Abstract
Interest in AYA cancer care has increased globally over the recent past; however, most of this work disproportionately represents white, heterosexual, middle-income, educated, and able-bodied people. There is recognition in the literature that cancer care systems are not structured nor designed to adequately serve people of colour or other equity-denied groups, and the structural racism in the system prevents prevention, treatment, and delivery of care. This work seeks to examine structural racism and the ways that it permeates into the lived experiences of AYAs in their cancer care. This article represents the first phase of an 18-month, patient-oriented, Participatory Action Research project focused on cancer care for racialized AYAs that is situated within a broader program of research focused on transforming cancer care for AYAs. Semi-structured interviews were completed with 18 AYAs who self-identify as racialized, have lived experiences with cancer, and have received treatment in Canada. Following participant review of their transcripts, the transcripts were de-identified, and then coded by three separate authors. Five main themes were identified using thematic analysis, including the need to feel supported through experiences with (in)fertility, be heard and not dismissed, advocate for self and have others advocate for you, be in community, and resist compliance.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 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; 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".