Insiders’ Insight: Discrimination against Indigenous Peoples through the Eyes of Health Care Professionals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Discrimination in the health care system has a direct negative impact on health and wellbeing. Experiences of discrimination are considered a root cause for the health inequalities that exist among Indigenous peoples. Experiences of discrimination are commonplace, with patients noting abusive treatment, stereotyping, and a lack of quality in the care provided, which discourage Indigenous people from accessing care. This research project examined the perspectives of health care providers and decision-makers to identify what challenges they see facing Indigenous patients and families when accessing health services in a large city in southern Ontario. Discrimination against Indigenous people was identified as major challenges by respondents, noting that it is widespread. This paper discusses the three key discrimination subthemes that were identified, including an unwelcoming environment, stereotyping and stigma, and practice informed by racism. These findings point to the conclusion that in order to improve health care access for Indigenous peoples, we need to go beyond simply making health services more welcoming and inclusive. Practice norms shaped by biases informed by discrimination against Indigenous people are widespread and compromise standards of care. Therefore, the problem needs to be addressed throughout the health care system as part of a quality improvement strategy. This will require not only a significant shift in the attitudes, knowledge, and skills of health care providers, but also the establishment of accountabilities for health care organizations to ensure equitable health services for Indigenous peoples.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it