A quantitative study exploring undergraduate nursing students’ perception of their critical thinking and clinical decision making ability while using apps at the point of care
Bibliographic record
Abstract
The purpose of this study was to explore how a smartphone app influences undergraduate nursing students’ perceptions of their critical thinking and clinical decision making ability at the point of care. Using a pretest-posttest approach, the findings suggest that there were no statistically significant differences in the participants’ perception of their critical thinking and clinical decision making ability over time. Statistically significant findings on four questionnaire items pertaining to participants’ perception in their ability to engage in evidence based practice over time suggests that experience with app, led the participants to believe the app provided them with the information they needed in order to engage in evidence based practice. Consequently, they were less likely to seek information from other sources. Although having learning resources available in clinical practice environments might enhance critical thinking ability, perhaps counterintuitively, the findings in this study suggest that having access to a clinical mobile app did not positively influence the participants’ perceived critical thinking ability. Nurse educators therefore, must teach students how to be active learners as well as role model the proper use of critical thinking skills. Students need to be reminded to use institutional policies and procedure manuals as well as other appropriate sources of information. Last, students need to see registered nurses use critical thinking and clinical decision making dispositions by asking comprehensive questions, exploring assumptions and inferences, and incorporating varying resources into their decisions.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".