Promoting Indigenous nursing student success in post-secondary education: A phenomenological study
Bibliographic record
Abstract
Background and aim: Indigenous students have a lower rate of post-secondary completion than non-Indigenous students. This is due to a variety of intrinsic and extrinsic factors. Current literature revealed a variety of concerns that were naturally divided under the themes of academic preparedness, cultural safety, intrinsic student factors, and student supports. This study examined the completion rates of Indigenous students within a nursing program, student associated success strategies, and predominant personal barriers to success. Using focus groups and interviews, the researchers examined the lived experiences Indigenous students at Saskatchewan Polytechnic in Western Canada.Methods: A qualitative design using a phenomenological approach was utilized to gather data. Focus groups and interviews with Indigenous students were conducted to gather personal perspectives and experiences. This data was then coded and themed.Results: The results of this study identified various strategies and supports that advanced and promoted Indigenous student success along with numerous barriers that Indigenous students felt impeded their success within the nursing program at Saskatchewan Polytechnic. The themes in this study included academic preparedness, cultural safety, resolve/resilience, confidence, social expectations, and financial support.Conclusions: There is a need to expand and develop support systems that enable Indigenous students to improve academic completion rates. Building confidence and a sense of belonging are important factors in improving student success from post-secondary programs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".