Surgical Experiences of Patients From the Circumpolar North: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: The Circumpolar North is an expansive region that includes the northernmost parts of the Earth, extending across the Arctic and Subarctic zones. This area, characterized by its geographical remoteness, harsh climate, limited healthcare infrastructure, and diverse patient population, creates unique challenges for the provision and delivery of surgical care. Despite existing research in this field, there is a lack of comprehensive reviews summarizing patients' distinct experiences when accessing surgical care. This scoping review, therefore, aimed to fill this gap by mapping the current literature on the surgical experiences of patients from the Circumpolar North. METHODS: A scoping review methodology was employed to identify relevant, original, peer-reviewed articles across seven databases, with no limits on publication dates. Article screening and data extraction were undertaken independently by two reviewers. An iterative data analysis process was employed to categorize findings from the included studies and identify common patterns, key insights, and knowledge gaps. RESULTS: A total of 17 studies were included in this review. Key factors influencing the surgical care experiences of patients from the Circumpolar North were identified across four domains: (1) logistical factors, including proximity to care centers, temporary accommodations, and financial costs; (2) psychosocial factors, such as experience of medical evacuations, separation from family, and reintegration into home communities; (3) cultural factors, encompassing navigating healthcare environments, language differences, and nonverbal communication; and (4) medical factors, including patient involvement, healthcare provider interactions, and continuity of care. Several studies also highlighted patients' experiences regarding innovative models aimed at improving locally based surgical care, such as telehealth and community-based strategies. CONCLUSION: This review summarized the literature on the surgical care experiences of patients from the Circumpolar North. It offers insights into improving healthcare interactions and systems to better serve this population. It also highlights significant research gaps, particularly regarding Indigenous patient experiences and the impact of medical evacuations across diverse surgical specialties. Addressing these gaps through future research is crucial for deepening our understanding of surgical experiences of patients from the Circumpolar North and developing more effective, culturally competent strategies to improve patient care.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".