Nursing geography as space and place: Providing critical care in Canada’s northern context
Bibliographic record
Abstract
Background: While there is a growing body of knowledge specific to understanding the experiences of nurses who work in various contexts in Canada’s northern territories, there continues to be a dearth of research specific to the care of critically ill patients and their families. Purpose: The purpose of this study was to further expand our understanding of how critical care nursing is experienced in the northern context. This study explored nurses’ experiences of providing critical care in all settings from purpose-built intensive care unit to community health centres. Study design: An interpretive descriptive approach was used. Methods: Telephone interviews were conducted with nine registered nurses currently employed in various settings, and three additional telephone interviews were used from an earlier pilot study. Interviews were transcribed and thematically coded. Findings: The following themes emerged: Always the Nurse; Working in Community; Must Haves and Managing Resources; Working Together – Even if We’re Not Together; and A Paradigm Shift: Working Up North. Conclusions: This study highlights the need to consider how nursing geography impacts nursing identity and how to best support nurses who provide critical care but, appropriately, may not identify as critical care nurses due to the expansive roles and responsibilities that some assume in nursing work outside of intensive care units. It also provides a strong incentive to establish relationships with those expanding our understanding of the northern context and what is required of nurses in this context to provide optimal care to patients, families and communities when and wherever critical illness is experienced.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".