“We’re on a Merry-Go-Round”: Reflections of Patients and Carers after Completing Treatment for Sarcoma
Bibliographic record
Abstract
Sarcoma is a rare cancer that has a significant impact on patients’ and carers’ quality of life. Despite this, there has been a paucity of research exploring the diverse experiences of patients and carers following sarcoma treatment. The aim of this study was to explore patients’ and carers’ reflections on life after treatment for sarcoma. A qualitative research design with a social constructionist epistemology was used. Participants included patients previously treated for sarcoma (n = 21) and family carers of patients treated for sarcoma (n = 16). Participants completed semi-structured interviews which were analysed using thematic analysis. Three primary themes were identified: “This journey is never going to be over”, “But what happens when I am better?”, and finding a silver lining. Participants represented sarcoma as having a long-term, and sometimes indefinite, threat on their life that they had limited control over. Conclusions: This study highlight the heterogeneous and ongoing needs of sarcoma survivors and their families. Patients and carers strove to translate their experiences in a meaningful way, such as by improving outcomes for other people affected by sarcoma. Parental carers in particular attempted to protect the patient from the ongoing stress of managing the disease.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".