Summary and thinking on application of best practice guideline in nursing teaching
Bibliographic record
Abstract
Objective To explore how to apply best practice guideline in teaching and the initial effects. Methods After deeply investigating courses, this study selected best practice guideline by combining with existing problems in teaching. Combined with the first-level and second-level class, a careful design scheme was conducted by each course groups and multiple rounds of project argumentation. After determining the program and teaching resources, each guideline application course group began to fully implement according to their own program. Results The best practice guidelines were successfully applied in 7 courses through various teaching forms including situation simulation, flipped classroom, case teaching as well as role playing which played an positive role in teaching content, teaching design and teaching evaluation. Conclusions In the whole process of implementation, teachers and students feel that the application of best practice guideline in nursing teaching is helpful to expand the frontier content of teaching, establish the evidence-based concept of teachers and students and initially cultivate critical thinking. It also plays a positive role in arousing the enthusiasm of nursing students and the comprehensive application of the knowledge they have learned. It makes evaluation methods flexible and diversified and is also beneficial to the real and objective response to the learning effect of nursing students. Key words: Teaching; Education, nursing; Best practice guideline; Evidence-based nursing; Critical nursing
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".