Healing journeys: stories of urban First Nations women overcoming trauma
Bibliographic record
Abstract
This Master of Social Work thesis focused on the healing journeys of urban First Nations women who have overcome trauma. The purpose of this research study was to develop a deeper understanding of healing and trauma from an Indigenous perspective. This Master of Social Work thesis created space for Indigenous knowledges so that Indigenous perspectives on the aspects of healing and trauma could be brought forward. At the centre of this created space were the voices of urban First Nations women and their shared stories of healing. This qualitative research study applied Indigenous research methodology, which also included narrative research methodology. In this study, the stories of five First Nations women who reside in an urban centre in Manitoba and who were well into their journeys of healing from trauma were explored. Manitoba First Nations traditional values, practice and protocol guided this thesis project to ensure that this research was conducted ethically and respectfully. The Medicine Wheel was used as a conceptual framework to understand the journeys of healing as well as the trauma experiences of the five women within the context of the life stages of human development. The meta-narratives and life narratives of the women provided accounts of their healing journeys. The findings of this research identified the following three overarching themes: living colonized lives, relationships, and healing paths. Recommendations were outlined for future social work research, practice, and education.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.018 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".