Bibliographic record
Abstract
The present study aimed to establish the credibility and attributed outcomes of an existing Indigenous mentorship (IM) model from the perspective of Indigenous mentees in health sciences and community research. Six mentees from mentorship networks associated with the Canadian Institute of Health Research’s IM Network Program participated in 1-2 hour long semi-structured interviews inquiring: 1) their resonance with the IM model, 2) personal stories related to the behavioural constructs in the model, 3) outcomes their mentors’ behaviours had on them, and 4) components they felt were missing from the model. Overall, the model resonated with participants. Of the model constructs, mentees discussed mentor behaviours associated with practicing relationalism the most frequently (26%), followed by fostering Indigenous identity development (23%), mentee-centered focus (21%), and imbuing criticality (16%). Advocacy (9%) and abiding by Indigenous ethics (5%) were addressed, but not given as much attention as the other constructs. Outcomes included positive career and work attitudes, engaging in more helping behaviours, motivation, overall well-being, and enhanced criticality. Recommendations to expand the model included incorporating: 1) additional mentor behaviours (transference of traditional knowledge, prayer, modeling resiliency, and engaging in trauma-informed practices), 2) higher-order dimensions (e.g., institutional impact), 3) specific mentee characteristics (e.g., age and gender), and 4) additional types of mentoring relationships (e.g., peer, multiple mentors). This research provides valuable insight to the IM model and IM theory more generally. This information can be applied to refine culturally appropriate mentorship practices, mentor selection and support, and evaluation of mentorship programs.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".