Non-clinical determinants of Medevacs in Nunavut: perspectives from northern health service providers and decision-makers
Bibliographic record
Abstract
A medevac involves the transport of a critically ill patient, usually by plane or helicopter, to access necessary and at times life-saving care, most often only accessible in urban centres. Medevacs are commonly used in resource-limited and geographically isolated areas in Canada. The objective of this study was to explore the determinants of medevac decision-making from the perspective of frontline care providers and decision-makers in Nunavut. For this purpose, we conducted a secondary analysis of 90 in-depth interviews. Findings indicate that medevacs can be the result of a number of intersecting factors, including the referring and receiving provider's experience, insufficient staffing in health centres, lack of access to diagnostic or treatment-related, and challenges related to recruitment and retention. An expanded scope of practice for frontline care providers, and a related lack of training and/or confidence in skills, only add to these challenges. Medevacs play an important role related to managing shifting community nursing workloads, which expands and contracts in response to local needs. Attention to structural issues, putting in place virtual peer support systems, resolving vacancies left by the lag between attrition and recruitment, increasing access to training, and local diagnostic and treatment equipment, might decrease reliance of medevacs.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".