Evaluation of the Clinical Learning Environment in Respiratory Therapy Education: Student Perceptions
Bibliographic record
Abstract
BACKGROUND: Clinical facilities are essential components not only of health care delivery systems, but also of health care education programs. The clinical learning environment (CLE) is important in training the future health care workforce. Because respiratory therapy (RT) is a practice-based profession, it is essential to integrate clinical education into RT education. RT education programs face several issues with respect to the need for preparing a proper CLE in various clinical settings. The purpose of this study was to determine the perceptions of RT students on the CLE of clinical facilities affiliated with an RT program at an urban state university and to determine the impact of education level on student perception of the CLE of clinical facilities. METHODS: We used an exploratory research design to evaluate the essential aspects of a CLE in RT education. An adapted Clinical Learning Environment Supervision and Nurse Teacher (CLES+T) evaluation scale was utilized to evaluate 34 RT students' perceptions of clinical facilities in RT education; 32 students participated in the survey, with a response rate of 94.1%. RESULTS: Responses included 2 groups of students: second-year undergraduates (68.8%), in which women accounted for 81% and men 19%, and graduate students (31.2%), in which women accounted for 60% and men 40%. Results obtained from the study indicated that both graduate and undergraduate RT students gave high median scores to the CLE, the supervisory relationship, and the role of clinical instructors. A statistically significant difference was obtained between the graduate and undergraduate students regarding multidimensional learning ( P = .043). We found that the majority of students positively rated the CLE in RT education. CONCLUSIONS: Education level influenced students' perceptions of the CLE of clinical facilities. Overall, respiratory therapy students rated their CLE experiences as positive and they were satisfied with the clinical instructor and team model.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".