Indigenous trauma and resilience: pathways to ‘bridging the river’ in social work education
Bibliographic record
Abstract
The traumatic effects of colonization on generations of Indigenous peoples and communities are referred to as intergenerational trauma. Alongside intergenerational effects of trauma experienced by Indigenous peoples and cultures across the globe is the capacity for individual and collective resilience, whereby an individual has good life outcomes despite having been subjected to situations with a high risk of emotional and/or physical distress. In North America and globally there have been calls to action for social work to find pathways toward reconciliation with Indigenous peoples. The purpose of this paper is to share the conceptual foundations of an innovative Master of Social Work program, currently in its sixth year. The program was designed to bridge Indigenous worldviews and social work by: creating links in the curriculum between neuroscience research in Western treatment modalities and Indigenous/traditional healing practices throughout the globe; fostering communication among all age groups; developing respect, kindness, and communication across all races; uncovering resiliency in understanding intergenerational trauma; understanding attachment difficulties created through colonization and rebuilding support systems; and creating learning objectives that address wellness. The objective is to prepare social workers to work with individuals, families and communities across the globe affected by intergenerational trauma.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".