Minority nursing students’ perception of their baccalaureate program
Bibliographic record
Abstract
Background: Review of the literature indicates that a number of barriers affect the success of minority nursing students and contribute to the high attrition rate. Identified barriers include feeling of loneliness, alienation, isolation, as well as academic and financial difficulties. To increase minority representation in the nursing work force and retention in nursing schools require understanding of perceived educational experience. Methods: The purpose of this study was to examine the perceived educational experience of minority nursing students at a southeast rural regional university in Georgia. These students were also enrolled in a comprehensive nursing workforce diversity project called SCRUBS Program, a comprehensive retention program designed to support minority nursing students’ academic achievement. Data were collected from forty-six participants of which 90% were African American; 5% Hispanic and 5% other. Of the 46 participants, 85% were female and 15% male. Results: Participants perceived their educational experience as “good, challenging, stressful, but overall positive and worth it.” Though participants noted that the nursing program curriculum was rigorous and overwhelming, available resources including mentoring, tutoring, peer socialization, and resources from the SCRUBS Program enhanced their educational experience. Conclusions: These findings have implications for retention of minority students in nursing education. The need to create a supportive and an inclusive cultural climate is essential for minorities’ academic success in nursing 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".