Holding space: a participatory exploration of first nations health professionals’ experiences supporting cancer patients through hospital-based treatment
Bibliographic record
Abstract
BACKGROUND: Colonial mechanisms continue to inflict trauma on Aboriginal and Torres Strait Islander peoples, the First Nations peoples of Australia. Consequence of this trauma is a disproportionately high rate of cancer mortality experienced among First Nations peoples and inequities in access to cancer services that are culturally responsive. There is a critical need for cancer care that supports First Nations peoples' holistic health and wellbeing. Engagement with First Nations health staff is a known element of culturally safe healthcare, however the experiences of and challenges facing First Nations staff working in cancer care are unclear. METHOD: Conducted as part of the What Matters to Adults Implementation (WM2A-Implementation) study, this paper presents the findings of a participatory approach to explore the experiences of four First Nations Health Professionals (FNHPs) providing holistic cancer care for First Nations peoples within cancer services located in public hospitals. Ten Yarning Circles were conducted by a First Nations researcher over a 12-month period. All were transcribed and a Knowledge Synthesis method employed a reflexive thematic analysis approach. Meaning making. FNHPs shared their experiences of working in a complex, highly pressured, and sometimes adverse space. FNHPs worked to support and advocate for their patients, create culturally safe spaces, and support and guide colleagues to the provision of culturally safe, patient-centred cancer care. Our knowledge synthesis revealed six intersecting themes that encapsulate their experiences: holding space; advocacy for patients; incorporating First Nations ways; serving your community; being everything to everyone; and the stigma of the role. DISCUSSION: These findings have implications for guiding cancer services to create an environment where First Nations staff are respected and given adequate resources, space, and support to deliver culturally grounded and supportive care to First Nations patients and their families. Specifically, services need to recognise the value of FNHPs in patient-centred care; balance this value with the burden on FNHPs; foster greater inclusion of First Nations culture and knowledges in mainstream healthcare; and actively focus on reducing racism and stigma facing FNHPs.
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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 | 0.029 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.026 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".