Indigenous students: Barriers and success strategies-A review of existing literature
Bibliographic record
Abstract
There are many factors that effect the post-secondary completion rate of Indigenous students. The Indigenous student completion rate is a reflection of the number of students entering post-secondary education but is significantly affected by withdrawal rates (institutional withdrawals and student voluntary withdrawals). In the Saskatchewan Polytechnic School of Nursing, the Indigenous student withdrawal rate was 4.2% higher than the total nursing student population. Lower success rates among Indigenous students is a concerning issue in nursing programs. Continuing to operate programs and teach in the same fashion is not improving success rates. The Truth and Reconciliation Commission of Canada: Calls to Action (2012) highlighted the need to examine strategies and develop policies to enhance Indigenous student success. To this end, recent literature was reviewed to determine trends among Indigenous nursing students, their struggles, and more importantly, the successful strategies currently being implemented. Indigenous peoples are not a homogenous group; rather, they are a mosaic of cultures, languages and nations. The authors examined the literature to determine key factors that enabled or prevented the success of post-secondary Indigenous students. Twenty-one articles on current research regarding Indigenous student success facilitators and barriers were examined. These articles encompassed research from Canada, the United States, Australia and New Zealand. The purpose of this literature review was to identify themes and gaps, drive positive change in education, and guide future research. The research team found four common themes: academic preparedness, cultural safety, intrinsic student factors, and student support.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".