Integration of traditional therapies for first nations people within western healthcare: an integrative review
Bibliographic record
Abstract
AIMS: To conduct an integrative literature review to reveal any evidence supportive of the integration of traditional therapies for First Nations peoples in Australia within a western healthcare model, and to identify which, if any, of these therapies have been linked to better health outcomes and culturally safe and appropriate care for First Nations peoples. If so, are there indications by First Nations peoples in Australia that these have been effective in providing culturally safe care or the decolonisation of western healthcare practices. DESIGN: Integrative literature review of peer-reviewed literature. DATA SOURCES: Online databases searched included CINAHL, Medline, Scopus, ScienceDirect InformitHealth, and ProQuest. REVIEW METHODS: Databases were searched for papers with full text available and published in English with no date parameter set. The PRISMA guidelines were used during the literature review and the literature was critiqued using the Critical Appraisal Skills tool. RESULTS: Seven articles met the inclusion criteria and were included in the review. Four articles selected were qualitative, two used a mixed method design, and one used a quantitative method. Six themes arose: (i) bush medicine, (ii) traditional healers, (iii) traditional healing practices, (iv) bush tucker, (v) spiritual healing, and (vi) therapies that connected to cultures such as yarning and storytelling. CONCLUSION: There is limited literature discussing the use of traditional therapies in Western healthcare settings. A need exists to include traditional therapies within a Western healthcare system. Creating a culturally safer and appropriate healthcare experience for First Nations people in Australia and will contribute to advancement in the decolonisation of current healthcare models.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".