Assessing the Conceptualizations of Coping and Resilience in LGBTQ2S+ People with Cancer: Working towards Greater Awareness in Cancer Care
Bibliographic record
Abstract
People with cancer may suffer negative psychosocial outcomes due to the challenges of cancer. LGBTQ2S+ people routinely experience negative psychosocial outcomes in health care settings, but have showcased resilience in the face of discrimination; however, this has never been studied in a cancer context. Thus, this study aims to assess coping and resilience in LGBTQ2S+-identifying people diagnosed with cancer using a strengths-based approach. A qualitative exploratory design was used. Ten self-identified LGBTQ2S+ people who have completed their cancer treatment were recruited. Participants completed clinical, health, and demographic questionnaires and, subsequently, semi-structured qualitative interviews. Conceptualizations of coping and resilience in the semi-structured interviews were analyzed using interpretative phenomenological analysis (IPA). Participants were members of various gender identities and sexual orientations. In addition to identifying needed LGBTQ2S+-specific resources, four narratives emerged: support networks, regaining control in life, conflicting identities, and traditional coping methods. Most participants' cancer journeys were characterized by a 'Second Coming-Out' phenomenon, where LGBTQ2S+ people with cancer use coping strategies, similar to those used when coming out, to produce resilience throughout their cancer journey. This work provides exploratory insight into LGBTQ2S+ people with cancer, but more research is required with a larger sample.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".