Sociocultural influences on healthcare inequalities among Australian Indigenous breast cancer patients
Bibliographic record
Abstract
Introduction Healthcare inequalities among Australian Indigenous people are significantly influenced by sociocultural factors. Understanding these influences and potential solutions benefits from insights into behavioural factors in parallel with social and cultural perspectives of beliefs and attitudes. Deeper analysis of breast cancer among Indigenous women is required to better understand factors and solutions to close the inequality gap. Method A systematic and critical review of peer-reviewed literature was undertaken using the MEDLINE (Pubmed) electronic database. After inclusion and exclusion criteria were applied, and critical appraisal undertaken, 20 articles were identified that aligned with breast cancer incidence or survival among Indigenous Australians. Result The analysis revealed three central themes; pathological factors, cultural factors and social factors. Data linkage strategies reveals inequality in both incidence and 5-year survival for Indigenous breast cancer patients. Both sociocultural behaviours and sociocultural attitudes and beliefs about health, illness and healing contribute to inequality among Indigenous breast cancer patients. Conclusion Poorer outcomes relate to Indigenous patients presenting with more advanced disease. Cultural and socioeconomic behaviours, attitudes and beliefs are significant barriers that create disparities between Indigenous and non-Indigenous women’s access to and engagement with breast cancer services.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".