Exploring the benefits and challenges of administrative clinical supervision in nursing and midwifery
Bibliographic record
Abstract
Objective: To explore the benefits and challenges of administrative clinical supervision at the unit.Methods: Clinical Supervision has been recognised as a core component of professional support for present-day nursing and midwifery practice. Effective administrative clinical supervision is beneficial to employees by providing support leading to the development of self-esteem, reducing emotional stress and improving commitment to an organisation’s vision and goals. Using aesthetic phenomenological approach, nurses' lived experience with administrative clinical supervision in a clinical unit of the hospital was explored and allowed in-depth description of administrative clinical supervision thoroughly. The participants were purposively sampled from six hospitals in Accra and comprised supervisors (n = 18) and supervisees (n = 12).Results: The benefits of administrative clinical supervision include reduction in infection rates, improved competence, client satisfaction, reduction in negligence, efficiency, accountability and feeling of being appreciated. Challenges of administrative clinical supervision were managerial challenges, limited time, interpersonal conflict with colleagues, and increased workload.Conclusions: Practical Implications: Administrative clinical supervision has implications for nursing and midwifery education and practice. This calls for measures that promote practitioners’ personal and professional development through fostering a supportive relationship and working alliance. Originality: This study employed the use of Aesthetic Phenomenology to tell the stories of clinical supervision from the personified interpretations unlike other studies that simply adopt descriptive phenomenology. It is the first of its kind in Ghana to the best of our knowledge.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".