Narratives of Survivorship: A Study of Breast Cancer Pathographies and Their Place in Cancer Rehabilitation
Bibliographic record
Abstract
The focus on cancer rehabilitation has increased, but breast cancer patients still report unmet rehabilitation needs. Since many women today will live long beyond their diagnosis, there are multiple challenges for the healthcare system in supporting these women in their new life situation. A more individualized approach is seen as necessary to optimize the rehabilitation for survivors. Pathographies, i.e., autobiographical or biographical accounts of experiences of illness, expose us to personal accounts of the journey through illness and treatment, offering us details, emotions, phrasings, and imagery from an individual perspective. In this literary study, we have analyzed two contemporary Swedish-speaking pathographies about breast cancer. In our analysis, we have presented perspectives on survivorship, and the authors' ways of conveying their breast cancer experiences through narrative. The pathographies envision the prominent impact the breast cancer has on the authors' lives. Narratives of survivorship have the potential to complement the more general medical knowledge with their nuanced and multifaceted stories of breast cancer. Learning from this type of material may improve the understanding of the complexity of breast cancer survivorship issues. This may be a way to become more attuned to identifying individual needs and preferences of breast cancer patients.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".