The space to make mistakes: allocating responsibility and accountability for nursing student-committed medication errors
Bibliographic record
Abstract
A medication error committed by a student nurse during a clinical placement often results in the student fearing its potential impact on the patient, unit staff, and the student’s educational journey. Student nurses must navigate two parallel systems during a clinical placement – the educational system and the healthcare system – and there can be confusion about what each requires of the student. Neither of these systems contain clear direction for managing student-committed medication errors and for allocating associated responsibility and accountability. This exploratory mixed methods study examines the process by which responsibility and accountability for a student-committed medication error is allocated and the factors that influence that allocation decision. It describes key features of an ideal allocation process and suggests reasons why the current allocation process often does not meet those requirements. Qualitative data were analyzed through interpretive description and quantitative data were analyzed using descriptive statistics. The results were situated, interpreted, and triangulated within a critical realism philosophical framework. An ideal post-error environment must incorporate a just culture. Since students must navigate both the educational institution and the healthcare facility environments during a clinical placement, a just culture must permeate both. However, students are instead colliding with a post-error environment that they perceive as not meeting key ideals of a just culture: fairness, transparency, minimization of fear, and dedication to learning. Findings of this study can be used to drive change that will better support those who are involved in a post-error process, and decrease the significant inconsistencies that are currently of particular concern.
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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 | 0.044 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".