The influence of clinical supervision and its potential for enhancing patient safety - Undergraduate nursing students’ views
Bibliographic record
Abstract
Objective: The clinical learning environment and supervision are crucial for the development of a professional stance and identity as well as for ensuring patient safety. This study aims to investigate the influence of clinical supervision provided to nursing students by nurse facilitators in hospital settings. An additional objective was to report the relationship between clinical supervision and patient safety. Methods: In this cross-sectional study, the sample consisted of 66 nursing students recruited after their clinical placement during the second year of the bachelor programme. Data were collected by means of questionnaires and analysed using a descriptive and explorative method. Results: Regarding the impact of clinical supervision, a moderately significant relationship was found between the three factors “Increased patient participation and problem solving”, “User involvement in terms of patient integrity” ( r = 0.48) and “Enabling patient and family member participation” ( r = 0.42) and the following Effects of Supervision Scale (ESS) factors; “Interpersonal skills” ( r = 0.47), “Professional skills” ( r = 0.50) and “Communication skills” ( r = 0.59). There was also a moderately significant relationship between the factors “Trust/Rapport” and “Influence of supervision” for the item “Supportive yet challenging relationships” ( r = 0.60). In addition, there was a strong correlation between the factors “Supervision advice/support issues” and “Influence of supervision” ( r = 0.73). The former correlated weakly with “User involvement”, i.e. , maintaining integrity ( r = 0.33).
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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 | 0.005 |
| 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 | 0.001 | 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".