Exploring Indigenous Traditional Healing programs in Canada, Australia, and New Zealand: A scoping review
Bibliographic record
Abstract
OBJECTIVE: To explore and catalog ways Indigenous Traditional Healing practices are supported within the mainstream healthcare system through policies and programs in Canada, Australia, and New Zealand. DATA SOURCES: A scoping review was conducted, guided by the PRISMA extension for Scoping Reviews. Databases for sources of information include CINAHL, Medline, Embase, Web of Science, Public Health ProQuest, Global Health EBSCO, iPortal, and grey literature. STUDY SELECTION: 2 reviewers screened the titles and abstracts of the studies for inclusion against the selection criteria independently. Studies that met the inclusion criteria were transferred to Covidence for further abstract and full-text review. DATA EXTRACTION: Of a total of 2,017 articles identified, 22 met the inclusion criteria for data extraction for this scoping review. Data items extracted include study title, authors, year of publication, publication type, publication source, support policy or program, health system or service, Indigenous Traditional Healing practices, and significant findings. DATA SYNTHESIS: 2 categories emerged from the analysis of the source of evidence. That is, healthcare systems and services with programs and policies supporting Indigenous Traditional Healing practices, and ways Indigenous Traditional Healing was adopted and utilized within the identified support programs. CONCLUSIONS: This study demonstrated the various ways Indigenous Traditional Healing practices are supported within the mainstream healthcare systems in Canada, Australia, and New Zealand. Indigenous Traditional Healing practices can be utilized as either the primary choice of treatment, to support Western biomedical treatment or through the adoption of Indigenous Traditional knowledge within the mainstream healthcare system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.015 | 0.030 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".