“Not even my husband knows that I have this [breast cancer]”: survivors’ experiences in accessing, navigating and coping with treatment
Bibliographic record
Abstract
PURPOSE: Nigeria has the highest burden of breast cancer (BC) in Africa. While the survival rates for BC are over 90% in many high-income countries; low-and middle-income countries like Nigeria have 40% BC survival rates. Prior studies show that the burden and poor BC survival rates are exacerbated by both health system and individual level factors, yet there is a paucity of literature on the experiences of BC survivors in Nigeria. Hence, this study explored the divergent and convergent experiences of BC survivors in accessing, navigating, and coping with treatment. METHODS: Participants (N = 24, aged 35 to 73 years) were recruited and engaged in focus group discussions (group 1, n = 11; group 2, n = 13 participants). Transcripts were transcribed verbatim and analyzed with inductive thematic analysis. RESULTS: Four themes were identified: "I am carrying this [breast cancer] alone," "Living my life," "'God' helped me," and "A very painful journey." Participants described how they concealed their BC diagnosis from family and significant others while accessing and navigating BC treatment. Also, they adopted spiritual beliefs as a coping mechanism while sticking to their treatment and acknowledging the burden of BC on their well-being. CONCLUSIONS: Our findings explored the emotional burden of BC diagnosis and treatment and the willingness of the BC survivors to find meaning in their diagnosis. Treatment for BC survivors should integrate supportive care and innovative BC access tools to reduce pain and mitigate the burdens of BC. IMPLICATIONS FOR CANCER SURVIVORS: The integration of innovative technologies for venous access and other treatment needs of BC is crucial and will improve survivorship. Non-disclosure of BC diagnosis is personal and complicated; hence, BC survivors need to be supported at various levels of care and treatment to make meaningful decisions. To improve survivorship, patient engagement is crucial in shared decision-making, collaboration, and active participation in care.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".