Cultural adaptation of an appropriate tool for mental health among Kanien'keha:ka: a participatory action project based on the Growth and Empowerment Measure
Bibliographic record
Abstract
We present a cultural adaptation of the Growth and Empowerment Measure (GEM) from the Kanien’kehá:ka people of Quebec (Canada). Our aim was to develop a culturally competent and safe tool to assess and promote well-being among this population. We followed a qualitative, collaborative, and participatory method that sought to benefit Indigenous participants and communities, while honouring their culture and philosophies. Twelve adults from health and community services of Kahnawà:ke participated in total five focus group meetings. We carried out a thematic analysis of the data collected through an advisory group that led a revision of the cultural and conceptual relevance of the tool and its content. The group integrated socio-culturally relevant elements and restructured the tool so that it reflected local well-being factors and showed its versatility of being an assessment tool and therapeutic support. A narrative and empowerement-driven approach, culturally based intervention, cultural safety and flexibility when using the instrument were considered successful strategies to improve wellness. This project provides valuable information about the perspectives of local Indigenous communities regarding mental health and factors of empowerment. Mutual understanding and integration of psychological and traditional knowledge can create a beneficial program to improve emotional, mental, spiritual, and physical well-being for the local population. It remains to be tested whether the Kanien’kehá:ka Growth and Empowerment Measure (K-GEM) is clinically useful in psychological and psychiatric intervention, and social and community services.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".