Bibliographic record
Abstract
BACKGROUND: The 'failure to fail' phenomenon has been reported in studies involving preceptors and students in nursing and in other practice professions, but it has yet to be the subject of exploration among clinical nursing instructors. Research has revealed that assigning a failing grade in a practice profession is not always a straightforward task; however, passing students who are incompetent in their nursing practice could have a deleterious impact on the quality and delivery of patient care. METHODS: The author interviewed eight clinical instructors who had failed unsatisfactory students to gain an in-depth understanding of each instructor's experience in evaluating these students. Gadamer's hermeneutic principles were used to interpret the interview texts and dialogue with the participants through the hermeneutic circle of understanding, use of the researcher's prior experience and openness to new understanding. FINDINGS: The experiences of the clinical instructors in this study suggest that they have struggled with their emotions, particularly when they were novices, during the multiple personal, professional and organisational challenges that they have encountered within their teaching roles and responsibilities. DISCUSSION: The difficulties and challenges highlighted in this study may offer insight for academic stakeholders in nursing schools on how to mitigate these issues through developing an onboarding orientation and mentorship programme that will support faculty member development in the evaluation of unsatisfactory student performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".