Depression or resilience? A participatory study to identify an appropriate assessment tool with Kanien'kéha (Mohawk) and Inuit in Quebec
Bibliographic record
Abstract
Purpose: We present a study on selection of a psychometric scale to be clinically used among Indigenous people with depression. Our aim was to select a psychometric tool for cultural adaptation with Mohawk and Inuit in Quebec. Methods: We selected three depression scales and three protective factor scales based on: strong validity for psychometric properties, evidence for good psychometric qualities across translations, avoidance of cognitively complex sentences, brevity, and clarity. We submitted the scales for consultation, and followed qualitative participatory methods with Mohawks of Kahnawake and Inuit from Nunavik living in an urban environment. We collected data through ten focus groups with advisory committees, and carried out a thematic analysis of the information. Results: The advisory groups considered the measurement scales to be unsafe. The major components that hindered their acceptance were: numeric rating, self-evaluation (versus supportive interaction), and a focus on symptoms rather than supportive factors. The participants preferred the Growth and Empowerment Measure due to its empowering approach. They voiced that it is necessary to develop a culturally sensitive and safe tool which facilitates interactions between the person and the practitioner. Conclusion: This project provides valuable information about the perspectives of local Indigenous peoples regarding mental health and factors of empowerment and resilience. The ideal tool should be flexible in terms of the content and its use as compared to the conventional psychometric strategies. A tool developed with the Indigenous perspective on wellbeing could be used in psychological and psychiatric intervention as well as in social and community services.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".