Bibliographic record
Abstract
The multicultural nature of Canada’s population continues to evolve with the growing number of culturally diverse groups within Canadian society. Despite efforts to improve the health of Canadians from all cultural backgrounds, it is well-established that health disparities are still continuing particularly for Indigenous and diverse cultural populations. One reason underlying such disparities is the lack of multicultural representation in the nursing profession. Recruiting, retaining, and graduating culturally diverse students from nursing programs will help to foster proportionate cultural representation in the nursing profession. As the profession attracts students from a variety of multicultural backgrounds with complex learning needs, it is important that the educational experience is situated in a culturally competent and safe learning environment. A learning environment that embraces positive faculty–student interactions in which there is a caring, sensitive, and committed attitude serves as an incentive for student success. It is recognized that the values, beliefs, and attitudes of educators, regardless of whether they are professors, clinical instructors, or preceptors is paramount to the promotion of student learning. Given the increasing necessity for nursing education for Indigenous people, this article offers a case study of how the nursing program at University College of the North, in northern Manitoba, provides a culturally competent and safe learning experience for Indigenous students. If nurses, educators, and other health care providers act in culturally and socially competent ways, the nursing workforce may become more diverse, health disparities may be altered, and the mandate to reduce and ultimately eliminate disparities may be realized. This report is part of a special collection from members of the University of the Arctic Thematic Network on Northern Nursing Education. The collection explores models of decentralized and distributed university-level nursing education across the Circumpolar North.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| 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".