Indigenous Health Research Mentorship within Post-Secondary Institutions in Canada, the United States, Australia, and New Zealand: A Scoping Review
Bibliographic record
Abstract
Indigenous peoples have been engaged in research since time immemorial, and have always acknowledged the power of their own knowledge systems, ways of being, and approaches. However, Indigenous peoples continue to be underrepresented in health research within academic institutions. There is an increased need for Indigenous leadership in health research, including greater Indigenous autonomy, mentorship, and self-determination in health research. This scoping review aims to explore Indigenous mentorship within Indigenous health research in post-secondary institutions in Canada, the US, New Zealand, and Australia. A review of empirical studies, case studies, reviews, commentaries, and grey literature was conducted. Four databases were used: Web of Science, PubMed, Native Health, and Google Scholar. Out of 1594 articles, 11 articles met the inclusion criteria. Four overarching themes were identified: (1) reciprocity: giving back to community; (2) supporting the development of research skills to build research capacity; (3) fostering a sense of belonging; and (4) building student ownership and confidence. The findings suggest that Indigenous mentorship is vital to creating supportive research environments for Indigenous students in the area of health sciences. Indigenous mentorship holds promise to address challenges faced by Indigenous scholars within post-secondary institutions, including intellectual, social, and cultural isolation, and can help to foster greater integration of Indigenous worldviews in Western-dominated academic settings and research systems. Future research should examine place-based mentorship opportunities for Indigenous students in community-based health research environments. Fostering Indigenous mentorship in health sciences is essential for advancing the health and wellbeing of Indigenous peoples and communities.
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 | 0.032 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.016 | 0.022 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| 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".