‘How Your Spirit Is Travelling’—Understanding First Nations Peoples’ Experiences of Living Well with and after Cancer
Bibliographic record
Abstract
As the number of people living with cancer increases, it is important to understand how people can live well with and after cancer. First Nations people diagnosed with cancer in Australia experience survival disparities relating to health service accessibility and a lack of understanding of cultural needs and lived experiences. This study aimed to amplify the voices of First Nations individuals impacted by cancer and advance the development of a culturally informed care pathway. Indigenist research methodology guided the relational and transformative approach of this study. Participants included varied cancer experts, including First Nations people living well with and after cancer, health professionals, researchers, and policy makers. Data were collected through online Yarning circles and analysed according to an inductive thematic approach. The experience of First Nations people living well with and after cancer is inextricably connected with family. The overall themes encompass hope, family, and culture and the four priority areas included the following: strength-based understanding of cancer, cancer information, access to healthcare and support, and holistic cancer services. Respect for culture is interwoven throughout. Models of survivorship care need to integrate family-centred cancer care to holistically support First Nations people throughout and beyond their cancer journey.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| 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".