Nursing students’ self-assessed levels of nursing skills at the time of graduation in a Japanese University during the COVID-19 pandemic: A retrospective observational study
Bibliographic record
Abstract
Background and aim: Clinical placements and on-campus practice are the core components of nursing skill acquisition, but the COVID-19 pandemic demanded fundamental modifications in the educational process for the nursing skill acquisition. The purpose of our research was to investigate how students at a nursing university assessed their own levels of nursing skills at graduation, relative to the target levels established by the Japanese government during the COVID-19 pandemic.Methods: This retrospective observational study included fourth-year students in 2020, 2021 and 2022 at the Faculty of Health Science and Nursing, Juntendo University, Japan all of whom had undertaken and completed the required clinical placements. A total of 141 skills required in nursing practice and corresponding target levels had been established by the Japanese government. Following their final clinical placement, students assessed their achieved level for each of the 141 skills.Results: Of the 141 skills, 20 (14.2%) were classified as “skills with difficult-to-achieve targets”, and 64 (45.4%) as “skills with easy-to-achieve targets.” All environmental adjustment skills were classified as “skills with easy-to-achieve targets.” Less than 40% of the nursing skills were classified as “skills with easy-to-achieve targets” in the subcategories of elimination support skills, activity and rest support skills, and respiration and circulation support skills.Conclusions: During the COVID-19 pandemic, it was difficult for nursing students to fully achieve the target levels of nursing skills. Nursing students who were forced to lose the opportunity to receive clinical placements and practice nursing skills in their university nursing education may be in serious need for generous support after graduation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".