Bridging the distance: understanding access to healthcare through stories from Gwich’in medical travellers in Northwest Territories
Bibliographic record
Abstract
In northern Canada, medical travel - the movement of patients to a larger centre to access healthcare services outside their home community - is a dominant feature of the healthcare system. This qualitative study explored the medical travel experiences of Gwich'in living above the Arctic Circle in the Gwich'in Settlement Area in Northwest Territories (NT). Data collection in 2020 comprised storytelling sessions with 10 Gwich'in medical travellers (6 female, 4 male). Using inductive and deductive methods with continual critical reflexivity, and guided by Gwich'in values, concerns about access to healthcare were found to be at the heart of each story. A broad conceptualisation of access was applied to understand and interpret the results according to six dimensions: accessibility, availability, affordability, adequacy, acceptability, and awareness. Situated within a context of colonialism, structural inequities and other factors relevant across the Circumpolar North, the results suggest that the NT medical travel policy framework provides only partial access to care. This article illustrates a need for healthcare and other government systems to think about policy and programmes in a more wholistic, equitable and relationship-centred way, which would help not only to bridge distances across geography, but also between peoples.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.020 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".