Buffering effects of social support for Indigenous males and females living with historical trauma and loss in 2 First Nation communities
Bibliographic record
Abstract
Globally, Indigenous mental health research has increasingly focused on strengths-based theory to understand how positive factors influence wellness. However, few studies have examined how social support buffers the effects of trauma and stress on the mental health of Indigenous people. Using survey data from 207 males and 279 females in 2 Ontario First Nations we examined whether social support diminished the negative effects of perceived racism, historical trauma and loss on depression and/or anxiety. Among females, having more social supports was significantly related to a lower likelihood of depression/anxiety, whereas greater perceived racism and historical losses were associated with a greater likelihood of depression/anxiety. For both males and females, childhood adversity was significantly related to a greater likelihood of depression/anxiety. Among females, a significant interaction was found between social support and childhood adversities. For females with low social support, depression/anxiety was significantly higher among those who had experienced childhood adversities versus those with none; however, for those with high level of social support, the association was not significant. The same relationships were not found for males. Possible reasons are that males and females might experience depression/anxiety differently, or the social support measure might not adequately capture social support for First Nations males.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".