Evaluating the Educational Environment of a Nursing School by Using the DREEM Inventory
Bibliographic record
Abstract
BACKGROUND: Educational Environment (EE) is considered as a key component of educational curriculum. AIM: The aim of this study was to evaluate the EE of Nursing and Midwifery School of Tehran University of Medical Sciences (TUMS) by using Dundee Ready Education Environment Measure (DREEM). METHODS: This cross-sectional descriptive study was conducted in 2013. Totally, 500 nursing or midwifery students were recruited to the study by using the quota sampling method. Study data were collected by using a demographic questionnaire and the Persian version of DREEM questionnaire. The reliability of questionnaire was confirmed by Cronbach alpha which was 0.876 and 0.68-0.866 for the whole questionnaire and its domains, respectively. The data were analyzed by using SPSS v. 21.0. RESULTS: Totally, 350 completely-filled questionnaires were included in the final analysis. Most of the participants were nursing students (79.7%), female (74.6%), single (86.0%), bachelor (86.9%), first-year (36.9%), with mean age of 22.5 years. The mean item score of the DREEM was 2.09±0.49 (104.39 from 200). Moreover, the mean item scores of the domains were as follows, perception of learning: 1.93±0.61; perception of teachers: 2.42±0.56; perception of educational atmosphere: 2.05±0.59; academic-self-perception: 2.06±0.65; and social-self-perception: 2.17±0.62. All domains were statistically significant except the perception of learning and educational atmosphere (p<0.001) CONCLUSION: Although the educational environment of the study setting was found to be positive, it requires improvements. Strategies such as adopting student-centered approaches, revising the educational curriculum, strengthening student-teacher relationship, being sensitive and responsive to students' educational needs, providing constructive feedback to them, and creating a comfortable, friendly, and supportive atmosphere can improve the educational environment.
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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 | 0.000 |
| 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) | 0.002 | 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".