Intergenerational Trauma: Convergence of Multiple Processes among First Nations peoples in Canada
Bibliographic record
Abstract
Stressful events may have immediate effects on well-being, and by influencing appraisal processes, coping methods, life styles, parental behaviours, as well as behavioural and neuronal reactivity, may also have long lasting repercussions on physical and psychological health. In addition, through these and similar processes, traumatic experiences may have adverse intergenerational consequences. Given the lengthy and traumatic history of stressors experienced by Aboriginal peoples, it might be expected that such intergenerational effects may be particularly notable. In the present review we outline some of the behavioural disturbances associated with stressful/traumatic experiences (e.g., depression, anxiety, posttraumatic stress disorder, and substance abuse disorder), and describe the influence of several variables (age, sex, early life or other experiences, appraisals, coping strategies, as well as stressor chronicity, controllability, predictability and ambiguity) on vulnerability to pathology. Moreover, we suggest that trauma may dispose individuals to further stressors, and increase the response to these stressors. It is further argued that the shared collective experiences of trauma experienced by First Nations peoples, coupled with related collective memories, and persistent sociocultural disadvantages, have acted to increase vulnerability to the transmission and expression of intergenerational trauma effects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".