The lived experience of a nursing course failure
Bibliographic record
Abstract
Background and objective: Nursing remains one of the fastest growing occupations according to the Bureau of Labor Statistics. Factors contributing to the ongoing nursing shortage including too few nursing faculty, limited clinical space, and sluggish growth in nursing program enrollment/capacity. Although most nursing programs are under pressure to accept as many qualified applicants as possible, as recently as 2019, U.S. nursing programs reported turning away over 91,000 qualified applicants due to insufficient faculty and classroom and clinical space. Because each spot in the program is valuable - the ability to help all students from admission through to graduation is critical. The purpose of this study was to identify the lived experience of students who had failed a nursing course. The information gathered from this group of students will enable nursing faculty to develop methods to help decrease failure for future students.Methods: This qualitative descriptive study utilized a phenomenological framework to determine the lived experience of baccalaureate nursing students who failed a nursing course. Semi-structured interviews were conducted during the summer and fall of 2021. Results: Literature reports numerous challenges of nursing school including difficulty maintaining a balance between life and studies. The findings reveal students may need additional help from faculty while navigating that balance. The findings of this study revealed four themes: student academic challenges, personal life events, testing difficulties, and coping with the aftermath of a course failure.Conclusions: Early identification of students at-risk for a nursing course failure and implementation of success strategies may decrease the incidence of nursing course failure. Recommendations are included.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".