Bibliographic record
Abstract
Within Canada, the Aboriginal population fares more poorly in almost every health determinant when compared to the rest of Canada (National Collaborating Centre for Aboriginal Health 2013).The Canadian Aboriginal population is also not as educated in comparison to the rest of Canada (Biswal 2008).It has the highest rates of addictions, chronic disease, and mental health issues in comparison to other cultures residing in Canada.This issue is worsened by the fact that many Aboriginal patients are treated poorly within healthcare due to racism, stereotypical assumptions, misunderstanding the Aboriginal culture, and having a lack of resources to adequately care for Aboriginal patients.Many healthcare providers do not have knowledge of Aboriginal experiences, culture, traditions, and the historical reasons behind their poor health outcomes and, consequently, do not treat Aboriginal patients appropriately.These challenges are troublesome because the health status of Aboriginal people is in dire need of improvement.The racism experienced by Aboriginal patients attending the Emergency Room in hospitals due to being seriously ill is a good example.Upon seeing a Physician, Aboriginal patients frequently receive improper care and a misdiagnosis because it is assumed that they are under the influence of alcohol, drugs, or suffer from addictions whereas in reality, many Aboriginal people in Canada live addiction free lifestyles (Health Council of Canada 2012).The goal of this project was to identify the variety of issues that Aboriginal people face in Canada's healthcare system.Solutions to these issues are discussed but the main insight is the categorization of these existent issues.The challenges that Aboriginal people face in the healthcare system are complex and require further examination to determine effective resolutions.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.062 | 0.014 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".