Multilevel perspectives in clinical learning environments' assessment: An insight on levels involved in planning nursing education
Bibliographic record
Abstract
Background: Clinical learning in nursing education is workplace-based and it involves both learning and organizational factors. Students could experience these factors both at individual and at group level. This study aimed to assess clinical learning environment as a multilevel phenomenon, by grouping students’ perceptions at different organizational levels. Method: A cross-sectional multilevel design has been conducted. 3 Italian Universities, 6 Hospitals and 73 wards have been involved in the study during 2013. Wards with at least 3 attending nursing students have been included. The sample involved 597 nursing students (average age 23.1 years, SD = 4.67 years; 72.6% females; 27.2% attending first year; 31.7% second year; 41.1% third year). Clinical Learning Environment and Supervision plus Nurse teacher ( CLES+T ) scale has been administered. Intraclass Correlation Coefficients (ICC) have been estimated at ward, at hospital and at University level. Results: All ICCs scores were above 0.10 and they indicated clinical learning environment as a multilevel phenomenon. The most pertinent level to multilevel research was the ward level. The nurse teacher scale was pertinent to Hospital and University level. Conclusions: Clinical learning environment is a multilevel phenomenon. These findings could enhance research develop ment in this field of studies. Practical implications suggest multilevel approach in order to detect the most effective organizational level to improve educational intervention.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".