Academic Success, Clinical Failure: Struggling Practices of a Failing Student
Bibliographic record
Abstract
In the deficit model approach to clinical evaluation, failures to achieve established academic or clinical standards are attributed to a flawed educational process or, more commonly, to nursing students' personal characteristics. Little is known about the meaning and significance of failing to students. Their perspective is lost among the plethora of clinical-like external criteria that predict the pathway to failure. Not all nursing students can be successful, yet when failure is the outcome, students' dignity, self-worth, and future possibilities must be preserved. Through a Heideggerian interpretative reanalysis of a individual example of an academically successful nursing student who failed clinically, this article discusses the consequences of disconnection in student-faculty relationships. The theme Preserving Personhood: Closing Down on a Future of New Possibilities is presented, as well as two subthemes--Struggling as Adopting a Chameleon Cloak and Struggling as Disconnecting Relations. A deeper understanding of students' clinical failure can help explain why failure, a socially constructed phenomenon, matters to nursing. Relational pedagogical practices to guide clinical educators in helping students at risk of failing are also discussed.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.028 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".