Fear of Cancer Recurrence among Aboriginal and Torres Strait Islander Women Diagnosed with Breast Cancer
Bibliographic record
Abstract
Little is known about the fear of cancer recurrence (FCR) severity, coping strategies, or quality of life impacts for Indigenous populations. This mixed-methods study aimed to (1) quantify FCR levels among Indigenous Australian (i.e., Aboriginal and Torres Strait Islander) breast cancer survivors and (2) qualitatively explore experiences of FCR and the coping strategies used. Nineteen participants completed the Fear of Cancer Recurrence Inventory (FCRI); ten also completed a semi-structured interview. Interview transcripts were thematically analysed. Average FCR levels (Mean FCRI Total Score = 71.0, SD = 29.8) were higher than in previous studies of Australian breast cancer survivors, and 79% of participants reported sub-clinical or greater FCR (FCRI-Short Form ≥ 13/36). Qualitative themes revealed the pervasiveness of FCR, its impact on family, and exacerbation by experience/family history of comorbid health issues. Cultural identity, family, and a resilient mindset aided coping skills. Greater communication with healthcare providers about FCR and culturally safe and appropriate FCR care were desired. This study is the first to assess FCR among Aboriginal and Torres Strait Islander breast cancer survivors, extending the limited literature on FCR in Indigenous populations. Results suggest FCR is a significant issue in this population and will inform the development of culturally appropriate interventions to aid coping and improve quality of life.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".