First Nations Australians and head and neck cancer: health professionals’ priorities for improving the pathway of care
Bibliographic record
Abstract
PURPOSE: I mprovements are needed in the care pathway for First Nations Australians with head and neck cancer (HNC); however, there is limited information to guide the development of culturally responsive care. The aim of this study was to use concept mapping to identify key priorities for service improvement in the HNC care pathway for First Nations Australians, through the perspectives of health professionals delivering care. METHODS: Health care staff (n = 27, including four First Nations Australians) reflected on their care delivery experiences and generated suggested actions to improve HNC care for First Nations Australians. Participants then rated these statements for importance and changeability and grouped them into similar concepts. The data then underwent multivariate analysis and multidimensional scaling to identify major conceptual domains. RESULTS: The final dataset included 73 unique statements, 21 from First Nations participants. Statements fell within nine cluster themes, in the following order of mean ranked importance: Person and family centred care, Continuity and care closer to home, Culturally safe care pathways, Staff cultural competency, Advocacy and support, Communication and connections, Culturally safe environment, Education and information, and Reducing financial burden. Of the 42 statements rated highest in importance, only 26 were perceived as both highly important and changeable, and eight of those were relating to improving Person and family centred care. CONCLUSIONS: Multiple areas for service improvement were identified, with varying levels of perceived changeability. The findings will inform further research involving co-design to enhance the care pathway for First Nations Australians with HNC.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".