The Practices of Critical Thinking Component and Its Impact in Malaysian Nurses Health Education
Bibliographic record
Abstract
The purpose of this research is to study the impact of the critical thinking component in the health education curriculum of nurses for patients with different health needs. Data for this research was gathered from mixed approaches, quantitative and qualitative approaches. For the quantitative approach 84 student nurses were selected randomly to represent the experiment and control groups in a private medical college in Northern part of Pennisular Malaysia. The 2 groups of students had been exposed to health education and clinical training in the wards as determined by the nursing curriculum. Following that, a treatment in the form of a critical thinking module which consisted of critical thinking related activities, was only given to the experiment group. Following the quantitative session, the qualitative approach was used. In this session, 5 student nurses were selected randomly and they were interviewed for 2 times, the first session was after the treatment was given and the second session was after clinical training with the patients. Besides these interviews, the patients concerned were also interviewed Findings from t-test and ANCOVA showed significant difference in the achievement between the experiment and control groups. In other words, these findings showed that there was a significant impact of the critical thinking component in the health education curriculum of nurses. Qualitative data findings showed that the respondents demonstrated thinking skills during their clinical training. Their patients too voiced individual views and perceptions. The students also felt that their thinking had improved after their induction to the critical thinking module. Key words: Critical Thinking. Curriculum. Nurses. Health Education. Patients. Decision-making
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".