Historicizing Health Inequities: Healing the vestiges of residential schooling
Bibliographic record
Abstract
Taking a social determinants of health approach to understanding the health issues of survivors of residential schooling and using a historical perspective as the lens through which to make sense of effective and ineffective healing mechanisms, I will highlight various interventions being undertaken with First Nations communities in Canada in response to historic trauma. This approach I will call historicizing health inequities, a way in which I have made sense of prevalent trends in public health and social inequity issues. To clarify, the approach to understanding health disparities called the ‘social determinants of health’ grew out of a recognition that health disparities are intimately related to, and caused by social inequities-- like poverty, racism, access to health care, education, housing, and so on and so forth. The social determinants of health are an effective mechanism to gain insight into the degree to which health disparities are determined by the experience of social inequities by various communities and within multiple contexts. My approach to understanding health issues also involves an additional element of looking at history to link the vestiges of historical inequities to current health disparities. This article will take a critical look at two different approaches to healing interventions carried out with First Nations people in response to historic trauma, a legacy of residential schooling. Through many case studies this essay will juxtapose both mainstream Western Psychology as well as healing interventions carried out by the Canadian Aboriginal community that employ community driven healing mechanisms.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.062 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".