Indigenous Mentorship for the Health Sciences: An Appraisal of a Contemporary Model by Indigenous Stakeholders
Bibliographic record
Abstract
Construct: In 2021, Murry et al. put forward a model of Indigenous mentorship within the health sciences based on the behaviors of Indigenous mentors toward their Indigenous mentees. This study explored mentees’ endorsements and/or criticisms of the IM model and how IM constructs and behaviors described in the model benefited them. Background: Models of Indigenous mentorship have been developed previously yet have not yet been empirically examined, restricting our ability to measure or make claims as to their consequences, correlates, and antecedents. Approach: Interviews with six Indigenous mentees asked about their: 1) resonance with the model, 2) stories related to mentors’ behaviors, 3) perceived benefits of their mentors’ behaviors on their journey, and 4) components they felt were missing from the model. Data were analyzed using qualitative content analysis. Findings: Overall, the model resonated with participants. Mentees told stories about mentors engaging in the IM constructs practicing relationalism most frequently, followed by fostering Indigenous identity development, utilizing a mentee-centered focus, and imbuing criticality, advocacy, and abiding by Indigenous ethics. Benefits included improved career and work attitudes, motivation, and overall well-being, engaging in helping behaviors, and enhanced criticality. Recommendations to expand the model included incorporating: 1) additional mentor behaviors (e.g., transference of traditional knowledge), 2) higher-order dimensions (e.g., the impact of the institution), 3) specific mentee characteristics (e.g., age and gender), and 4) additional types of mentoring relationships (e.g., peer, multiple mentors). Conclusions: This study showed that Murry et al.’s model resonated with primary stakeholders (i.e., Indigenous mentees), that Indigenous mentorship behaviors have perceived consequences that are important for adjustment, and ways the model is limited or mis-specified. This information can inform mentor practices, selection and support, and program evaluation.
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 | 0.035 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| 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".