Exploring paramedic care for First Nations in Alberta: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Prior work has shown that a greater proportion of First Nations patients than non-First Nations patients arrive by ambulance to emergency departments in Alberta. The objective of this study was to understand First Nations perspectives on transitions in care involving paramedics, and paramedic perspectives on serving First Nations communities. METHODS: Participants for this participatory qualitative study were selected by means of purposive sampling through author networks, established relationships and knowledge of the Alberta paramedicine system. First Nations research team members engaged First Nations community organizations to identify and invite First Nations participants. Four sharing circles were held virtually in July 2021 via Zoom by the Alberta First Nations Information Governance Centre. We analyzed the data from the sharing circles using a Western thematic approach. The data were reviewed by Indigenous researchers. RESULTS: Forty-four participants attended the 4 sharing circles (8-14 participants per circle), which ranged from 68 to 88 minutes long. We identified 3 major themes: racism, system barriers and solutions. First Nations participants described being stereotyped as misusing paramedic systems and substance using, which led to racial discrimination by paramedics and emergency department staff. Discrimination and lack of options to return home after care sometimes led First Nations patients to avoid paramedic care, and lack of alternative care options drove patients to access paramedic care. First Nations providers described facing racism from colleagues and completing additional work to act as cultural mentors to non-First Nations providers. Paramedics expressed moral distress when called on to handle issues outside their scope of practice and when they observed discrimination that interfered with patient care. Proposed solutions included First Nations self-determination in paramedic service design, cultural training and education for paramedics, and new paramedicine service models. INTERPRETATION: First Nations people face discrimination and systemic barriers when accessing paramedicine. Potential solutions include the integration of paramedics in expanded health care roles that incorporate First Nations perspectives and address local priorities, and First Nations should lead in the design of and priority setting for paramedic services in their communities.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".