The effects of concept mapping on student nurses’ learning of medical-surgical nursing
Bibliographic record
Abstract
Background and objective: Many student nurses are weak in studying medical-surgical nursing because higher-order skills are required to understand and incorporate prior knowledge with new knowledge. Hence, this study tried to employ concept mapping (CM) in teaching one medical-surgical nursing course to enhance their learning. The aims of this study are to explore the effects on student nurses’ learning experience and examine the difference in academic performance of students who learned this subject by using CM and those who did not.Methods: This was a mixed research study conducted in 2017. The overall grade and pass rate were used to compare the differences between CM and non-CM groups. Moreover, focus group discussions after semesters were used to explore the impact of CM on student nurses’ learning.Results: The difference of marks between the CM group (M = 77.90 and SD = 8.09) and non-CM group (M = 57.56 and SD = 10.16) was statistically significant (p = .000) with a large effect (Cohen’s d = 2.21). Twenty-six student nurses were interviewed in focus group discussions. The advantages and shortcomings of CM were identified. After new and prior knowledge was bridged, students used their own perceived effective method to re-organise knowledge and enhance their memory to prepare for written examinations.Conclusions: To conclude, CM seems able to improve students’ academic performance, and students gained a good understanding of the relationships between concepts in medical-surgical nursing, especially for the students who were new to the subject. The results of this study will provide insights for nurse educators who teach Chinese student nurses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".