The effect of problem-based learning on nursing students' decision making skills and styles
Bibliographic record
Abstract
Decision making skill is deemed to be a key feature of the nurse’s role in today’s health care organizations. Thus, educators should use innovative teaching strategies that grow students’ competence in problem-solving and decision making skills like problem-based learning (PBL). The aim of this study is to determine the effect of problem-based learning on nursing students' decision making skills and styles. A quasi-experimental research design was utilized. The sample consisted of 84 students from the fourth year in the Faculty of Nursing, Minia University. Two tools were used: Nursing Students’ Decision Making Skills Scale and Nursing Students’ Decision Making Style. This study revealed that the mean scores of decision making skills in the study group students increased before and after applying PBL (before: 71 + 8.5, after: 116.3 + 10.4) with a statistical significant difference (p = .001). There was no statistical significant differences between the study and control groups (p = 1.000) before intervention. The most dominant decision-making style among the study and control groups in relation to before and after applying PBL was a behavioral decision style with no statistical significant differences. This study concluded that using PBL has a curial role in developing and improving nursing students' decision making skills; however, it has no effect on decision-making style.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".