Bibliographic record
Abstract
BACKGROUND: Identity negotiations of people living with cancer have been shown to be significant psychosocial challenges throughout cancer trajectories but have not been adequately explored among young adults with cancer. Narrative approaches might help to reveal moments of (dis)empowerment that affect their identity negotiations. OBJECTIVE: The aim of this study is to explore how young adults speak to their identities in relation to their narratives of having cancer and receiving care. METHODS: A total of 21 young adults (18-45 years old) provided cancer narratives through semistructured life history interviews. Thematic narrative analysis was used to determine how participants represented themselves in their stories. RESULTS: Participants used a wide diversity of identities well beyond those most familiar in dominant discourses (eg, patients, survivors, and fighters), and their identities frequently changed at significant "turning points" in their narratives, especially in relation to good and bad experiences of care. CONCLUSIONS: Cancer-related identities often undergo personal and social negotiation over time, and not just among young adults still feeling the effects of treatment. Psychosocial oncology could take further steps toward incorporating this fluidity and multiplicity within the discipline's discourses of identity. IMPLICATIONS FOR PRACTICE: The identities gathered here may contribute to a more comprehensive toolkit of narrative resources for empowering young adults (and others) with cancer, serving as a starting point for negotiating identities with their care providers. Our findings raise questions about which identities should be fostered and how healthcare professionals might be (unknowingly) involved in patients' identity negotiations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".