Clinical supervision factors as perceived by the nursing staff
Bibliographic record
Abstract
Objective: Assess clinical supervision factors as perceived by nurses and first line nurse managers at Assiut University Hospital, and explore the relationships among personal characteristics and clinical supervision factors of studied nurses and first line nurse managers.Methods: A descriptive design was utilized in Medical and Surgical departments at Assiut University Hospital for A convenience sample of first line nurse managers (N = 30) and nurses (N = 151) by using study tools for nurses included two parts: 1) personal characteristics data sheet; 2) clinical supervision factors, and Study tool for first line nurse managers included two parts: 1) personal characteristics data sheet; 2) clinical supervision factors.Results: The highest mean scores were in trust and rapport & Supervisor advice and support of clinical supervision factors among the studied nurses. While among first line nurse managers' the highest mean scores were in improved care and skills & personal issues and reflection of clinical supervision factors.Conclusions: The most important clinical supervision factors which had the positive correlations were between finding time and ward atmosphere with age & years of experience with importance and value of clinical supervision among the studied first line nurse managers, while there was a negative significant correlation between age and trust and rapport & leadership style of the ward manager among the studied nurses. Nurse Managers should direct, monitor and evaluate the staff nurses through scientific standards of supervision as recommendation for the study results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".