Application value of Calgary-Cambridge Guide joint case teaching in the course of community health education and health promotion
Bibliographic record
Abstract
目的 探讨卡尔加里-剑桥指南联合案例教学在《社区护理学》课程中的应用价值。 方法 选择在湖州市中心医院实习的2016级四年制护理本科学生120名为研究对象，按照随机抽签法分为观察组和对照组60名，在《社区护理学》课程讲解时分别采用卡尔加里-剑桥指南联合案例教学及传统教学法。比较两组成绩和实践表现评分。 结果 观察组的平均成绩、综合成绩优良率分别为(86.35±4.26)分、96.67%，明显高于对照组的(79.18±3.97)分、81.67%(t＝9.538、χ2＝6.988，均P<0.05)。观察组的理论知识、实践能力、应变能力、信息能力、思维能力、沟通能力等各项得分均高于对照组(t＝9.642、10.521、10.795、8.055、6.135、7.296，均P<0.05)。 结论 卡尔加里-剑桥指南联合案例教学在《社区护理学》课程教学中，针对性强，能够增强学生的沟通技巧，具有可行性。
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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 | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".