A Descriptive Analysis of the Impact of Moral Distress on the Evaluation of Unsatisfactory Nursing Students
Bibliographic record
Abstract
BACKGROUND: Nurse educators assume a difficult role when evaluating unsatisfactory students, including those at risk for failure in clinical and classroom settings. While the decisional dilemma inherent in evaluating unsatisfactory students has been well documented in literature, little is known about how moral distress impacts the nurse educators' decisions regarding whether to pass or to fail unsatisfactory students. PURPOSE: This article aims to provide a descriptive analysis of the moral dilemmas and the potential impact of moral distress experienced by nurse educators when evaluating the performance of unsatisfactory students in clinical and classroom courses. METHODS: Nathaniel's theory of moral reckoning guided the descriptive analysis of six studies to understand how nurse educators work through moral dilemmas, make decisions, and provide justification for their decisions when evaluating the performance of unsatisfactory students. FINDINGS: Nathaniel's theory has been shown to be helpful in discussing the dilemma of evaluating unsatisfactory students, and it is a suitable framework for nurse educators in working through their dilemmas as a form of structured reflection. PRACTICE IMPLICATIONS: The outcomes of this descriptive analysis highlight the need for educational administrators to provide support to undergraduate nurse educators experiencing moral distress in this type of situation.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".