Effect of concept mapping on problem solving skills, competence in clinical setting and knowledge among undergraduate nursing students
Bibliographic record
Abstract
Background and objective: Concept mapping is a powerful instructional tool in the clinical settings that improves competency of undergraduate nursing students in interprets situations, problem solving, decision making and critical thinking in different circumstances. Also enable them to provide optimal comprehensive care for clients. This study was conducted to evaluate the effect of concept mapping on problem solving skills, competence in the clinical settings and knowledge among undergraduate nursing students.Methods: Design and Setting: A quasi-experimental design was used and data were collected from Medical & Surgical Nursing Department and Community Health Nursing Department labs in the Faculty of Nursing, Tanta University. Sample: Random sample of 60 undergraduate nursing students which are selected by using simple random method who are divided into; thirty students from second year and thirty students from fourth year. Tools: Four tools were used for data collection: Tool (I): Structure questionnaire sheet to assess students’ knowledge regarding concept mapping, Tool (II): Case study rubric for assessing concept map, Tool (III): Problem solving skills assessment sheet and Tool (IV): Perception of students about using of concept map.Results: As a result of this research, there were significant improvements among students knowledge about concept map, simulation case study rubric and problem solving skills, in addition to more than three quarter from students had positive perceptions regarding application of concept mapping in the clinical settings.Conclusions and recommendation: Based on the findings of the study, there were significant improvement in the score of knowledge, simulation case study rubric and problem solving skills post application of concept mapping in the clinical setting. Therefore, it is necessary to improve wide-spreading of concept map training guidelines for large number of undergraduate nursing students at the level of the nursing faculties.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".