Approaches to Ethically Studying Indigenous Engineering Student Mental Health
Bibliographic record
Abstract
Background: As Canadian engineering programs attract more Indigenous students, it is important we understand the mental health needs and experiences of Indigenous students so we can provide the support they need. The purpose of this paper is to share the approach to designing a study exploring Indigenous engineering student mental health and to highlight the considerations required when doing research in collaboration with Indigenous peoples. This paper explores the use of Indigenous methodologies, Two-Eyed Seeing, and the medicine wheel in the study design. Preparing to do research with Indigenous students, particularly as researchers with European ancestry, requires significant planning and reflection. Key features of this include taking First Nation Principles of Ownership Control Access and Possession (OCAP) training and committing to the OCAP principles, using reflexivity as a researcher, and ensuring participant safety when exploring sensitive topics. As we advance truth and reconciliation in academia, it is crucial that we do so with reflexivity, respect, and reciprocity, prioritizing anti-oppressive practices and balancing Indigenous ways of knowing with western knowledge.
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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 | 0.158 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.032 | 0.103 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.011 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 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".