A Multimethod Approach to Resilience Against Alcohol Use, Depression, and Suicide among Indigenous Youth in a Northern Quebec Community
Bibliographic record
Abstract
Colonization, historical loss and intergenerational trauma have given rise to mental health disparities among Indigenous communities. For over 100 years, assimilation policies have directly targeted Indigenous youth. While many Indigenous youth have thrived despite the experience of intergenerational trauma and ongoing colonization, problems with alcohol use, depression, and suicide risk continue to be reported. Yet, little research has looked at the temporal sequence of these mental health problems among Indigenous youth. In turn, interventions have often been based off of research among non-Indigenous youth, which have been limited at best. In turn, there is a need to return to Indigenous ways of knowing in order to promote the well-being of Indigenous youth. Using quantitative (Study 1) and qualitative (Study 2) studies, the goal of this dissertation was to develop a community-specific model of alcohol use, depressive symptoms, and suicide resilience among Indigenous youth in one Northern Quebec community. Study 1 (N=110) utilized a longitudinal design to examine change in alcohol use and negative affect (a symptoms of depression) and reciprocal associations in a sample of Indigenous youth. Results demonstrated that when an Indigenous adolescent drank more alcohol than expected at one time point, they reported higher levels of negative affect than expected at the following assessment. This may suggest that drinking alcohol precedes negative affect. Study 2 (N=14) utilized semi-structured interviews with community members to understand alcohol and suicide resilience from an Indigenous perspective. Through the voices of Indigenous people in the community, colonization was identified as the primary problem that led to alcohol use and suicide risk. Complementary to Study 1, most of the participants highlighted that drinking alcohol precedes suicidal ideations and behaviours. Connecting as a community and returning to living off of the land is where the participants believed recovery would be found. Taken together, both studies shed light on understanding alcohol use, negative affect and suicidality rooted in systemic factors and a continued call for supporting Indigenous peoples in revitalizing their cultures.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".