Bibliographic record
Abstract
Background: Academic success in programs of nursing requires successful completion of didactic and clinical activities. Failure, in didactic situations, is objectively determined. Clinical failure is determined subjectively, which may expose the competency and reputation of the clinical faculty. This scenario can result in a hesitancy, or a reluctance to fail a student. Graduation may occur in the presence of limited clinical competency resulting in new graduates who are not adequately prepared for professional nursing practice.Methods: Exploring the concept of reluctance to fail will provide a conceptual definition based on uses of the concept found in research studies. Walker and Avant (2011) describe an eight step concept analysis process which will be utilized to determine the defining attributes, antecedents, consequences and empirical referents of the concept of reluctance to fail.Results: The result of this concept analysis is a conceptual model depicting reluctance to fail as a circular phenomenon with various elements. Guided by the intervention needed to address the deficiency, these elements may be placed in one of three categories: education of faculty, role modeling, and peer support.Conclusions: Education of clinical faculty will diminish the unwillingness, and hesitancy elements. Role modeling activities will prevent fear as rationale for reluctance to fail. Peer support provides emotional support when guilt for assigning a failing grade occurs. Future research must be conducted to identify factors responsible for faculty reluctance to assign failing grades, as well as the effectiveness of these interventions.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".