Indigenous Mentorship in the Health Sciences: Actions and Approaches of Mentors
Bibliographic record
Abstract
Phenomenon: Indigenous and non-Indigenous scholars have called for mentorship as a viable approach to supporting the retention and professional development of Indigenous students in the health sciences. In the context of Canadian reconciliation efforts with Indigenous Peoples, we developed an Indigenous mentorship model that details behavioral themes that are distinct or unique from non-Indigenous mentorship.Approach: We used Flanagan’s Critical Incidents Technique to derive mentorship behaviors from the literature, and focus groups with Indigenous faculty in the health sciences associated with the AIM-HI network funded by the Canadian Institutes of Health Research. Identified behaviors were analyzed using Lincoln and Guba’s Cutting-and-Sorting technique.Findings: Confirming and extending research on mainstream mentorship, we identified behavioral themes for 1) basic mentoring interactions, 2) psychosocial support, 3) professional support, 4) academic support, and 5) job-specific support. Unique behavioral themes for Indigenous mentors included 1) utilizing a mentee-centered approach, 2) advocating on behalf of their mentees and encouraging them to advocate for themselves, 3) imbuing criticality, 4) teaching relationalism, 5) following traditional cultural protocols, and 6) fostering Indigenous identity.Insights: Mentorship involves interactive behaviors that support the academic, occupational, and psychosocial needs of the mentee. Indigenous mentees experience these needs differently than non-Indigenous mentees, as evidenced by mentor behaviors that are unique to Indigenous mentor and mentee dyads. Despite serving similar functions, mentorship varies across cultures in its approach, assumptions, and content. Mentorship programs designed for Indigenous participants should consider how standard models might fail to support their needs.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".