Students and teachers’ perception of an effective clinical nurse teacher characteristics: A comparative study
Bibliographic record
Abstract
Objective: This study aims to determine the level of perception of nursing students and teachers on the effective clinical nurse teacher characteristics and find if there is a significant difference between the level of perception of nursing students and teachers according to their demographic features. Methods: This study employed a descriptive - comparative design. Simple random sampling was undertaken and a questionnaire developed by Brown (1981) was utilized in gathering information from the participating 244 nursing students and 46 teachers as respondents. Frequency, percentage, t-test, F-test in Statistical Package for the Social Sciences (SPSS) version 22 was used in the analysis of data.Results: Most of the participating nursing students in the study are aged 18-22-year-old, female, and unmarried while most teachers were more than 46-year-old, female, and married. Among the three indicators of effective clinical teacher characteristics, the teachers consider professional competence, relationships with the students’ most important, and personal attributes as very important while the nursing students perceived all as very important. A significant difference exists in the level of perception of both groups of respondents on different indicators. However, in certain demographic profile, specifically gender and marital status there seem to be no significant difference but it exists with age.Conclusions: Both nursing students and nurse-teachers perceived that an effective clinical teacher characteristic has a significant influence on the clinical learning course of students. The perception varies significantly with age and this would suggest that as the nurse grows older and gain more experience his/her perceptions matures.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".