Views of second year nursing students on impediments to safety in the clinical setting: Q-methodology
Bibliographic record
Abstract
Background: Little is known about novice students’ perspectives of safety in clinical learning. This gap prevents a comprehensive understanding of their efforts to demonstrate clinical competence while securing safety for stakeholders in increasing complex practice environments. The purpose of this study was to describe impediments to safe clinical learning as perceived by second year students enrolled in a baccalaureate nursing program. Methods: Q-methodology was used to systematically elicit multiple viewpoints about unsafe clinical learning circum- stances. Across two program sites in northern Ontario, Canada, 73 second year students sorted 43 theoretical statement cards identifying unsafe clinical practices and situations. Centroid factor analysis and varimax rotation yielded correlations between participants who held similar and different viewpoints about impediments to safety in clinical learning. Results: Three discrete perspectives and one consensus perspective constituted second year students’ description of unsafe clinical circumstances. The discrete viewpoints were unprepared for role enactment, unsupported learning, and breached standards. There was consensus that a failure to demonstrate patient protection compromised clinical safety. The findings characterized unsafe clinical milieus as a combination of student, educator and programmatic accountability issues. Conclusions: The shared perspectives of novice learners call attention to student preparedness, learning support and adherence to disciplinary standards. Educators and clinicians are compelled to address these issues for the development of conscientious novices within a culture of safety.
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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 | 0.031 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".