Learning Needs of Nurses Working in Canada's First Nations Communities and Hospitals
Bibliographic record
Abstract
BACKGROUND: What are the learning needs of nurses providing services to Canada's First Nations Communities and Hospitals? First Nations (or Indian Band) are similar to communities except some comprise more than one geographic Native community. Aboriginal (or Native) individuals are members of the North American Indian, Inuit, or Métis peoples of Canada; those who reported being a Treaty or Registered Indian (with the Federal Government); or those who are members of an Indian Band/First Nation. METHOD: A Canada-wide survey was completed to determine the learning needs of nurses working with Canada's Aboriginal persons. RESULTS: Nurses indicated both broad and specific aspects of their clinical practice, which were important to their continuing education (CE) needs. Broad thematic areas for continuing education included the following: emergency/acute care and obstetrics/gynecology clinical skills, health and physical assessment, mental health, and prenatal and postnatal care. Specific areas nurses cited for CE included issues related to: victims of violence; non-compliant clients; substance abuse; and fetal alcohol syndrome. CONCLUSION: This study examined the learning needs of nurses working with Canada's Native people and provided a basis for comparing and contrasting CE issues of these nurses to other nurses working in remote locations around the world.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".