Community health nurses: the mainstay of remote primary care in the Canadian North
Bibliographic record
Abstract
Since the 1950s when aboriginal people in the Canadian North began to be settled in communities health care has been provided by a variety of agents including missionaries, RCMP, trading post staff among others. This evolved finally into a system of 'Nursing Stations' staffed by 'Community Health Nurses' in small, remote communities across the Canadian Arctic and sub-Arctic. These nurses worked independently as advanced practice nurses with varying degrees of support from general practitioner/family physicians. They are often the only care provider in the community, and over a thousand kilometers away from a tertiary centre, they are obliged to deal with whatever medical problems present to them. Community Health Nurses work in large numbers in Nunavut and the other Northern Regions of Canada. These nurses work with family practitioners in a more collaborative relationship as opposed to the more traditional consultative physician - nurse model that is seen in other health care settings. This presentation will review the close working relationship between Community Health Nurses and family physicians in the Canadian North; will reference the potential for new classes of providers including Nurse Practitioners and Physician Assistants; and will identify some organizational and technological innovations that may enhance the working relationship between the advanced practice nurses and family physicians; and identify some challenges facing this critical group of health care providers: lack of appropriate training, outdated staffing models, high rates of staff turnover.This abstract was presented at the Innovative Solutions in Remote Healthcare - 'Rethinking Remote' conference, 23-24 May 2016, Inverness, Scotland.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".