“We Are Bridging That Gap”: Insights from Indigenous Hospital Liaisons for Improving Health Care for Indigenous Patients in Alberta
Bibliographic record
Abstract
This thesis explores the insights of five Indigenous Hospital Liaisons working in Alberta Health Services hospitals to improve health care for Indigenous patients. Indigenous people in Canada often encounter difficulties and obstacles when accessing health care. Previous studies have described the negative experiences that many Indigenous people encounter within hospitals and other health care facilities. Limited research in Alberta prevents an understanding of context-specific barriers that may exist. Additionally, the insights of Indigenous Hospital Liaisons are a new area of research. These frontline employees work directly with Indigenous patients to help them navigate barriers and ultimately improve their hospital experiences. This research uses a strength-based approach to highlight the dedication that Indigenous Hospital Liaisons have to Indigenous patients and provide a more positive image of Indigenous people. In addition to pointing out barriers they believe are commonly encountered by Indigenous people in Alberta, the Indigenous Hospital Liaisons in this research highlight existing resources that can benefit Indigenous patients. They also share their suggestions for changes that may improve hospital experiences for Indigenous people. The findings of this research provide a welcome contrast to the mass of negative and often deficit-based research that dominates the Indigenous health literature and suggest a more positive and optimistic view of the future of health care for Indigenous people in Alberta. Specific recommendations are provided based upon the findings and suggestions for future research involving Indigenous Hospital Liaisons are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.038 | 0.014 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".