Lessons learned from clinical course design in the pandemic: Pedagogical implications from a qualitative analysis
Bibliographic record
Abstract
AIMS: The purpose of this study was to examine clinical pedagogy based on experiences of changes and adaptations to clinical courses that occurred in nursing education during the pandemic. Beyond learning how to manage nursing education during a pandemic or other crisis, we uncover the lessons to be learned for overall improvement of nursing education. DESIGN: Qualitative descriptive analysis using semi-structured interview data with baccalaureate nursing students. METHODS: Data were collected in the spring of 2021 using semi-structured interview with 15 participants. Transcribed text was analysed using thematic content analysis. The COREQ checklist was used to guide our reporting. RESULTS: Three themes were identified related to course design in clinical courses for nursing students: the role and limitations of simulation, competency evaluations and career implications. Students expressed some concern over not 'finishing hours', loss of in-person clinical experiences and their reduced exposure to different clinical settings. CONCLUSION: To prepare work-ready nurses, educators need to keep in mind the trends, issues and demands of future healthcare systems. Simulation may have been a temporary measure to achieve clinical competence during the pandemic but needs to be of high-quality and cannot meet all the expected learning outcomes of clinical courses. Exposure to different patients, families and communities will ensure that the future nursing workforce has experience, socialization, competence, and desire to work in various clinical settings. Competency evaluation similarly needs to be robust and objective and consider the role and perception of hours completed. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. Participants were nursing students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".