Clinical supervision at a magnet hospital: A review of the preceptor-facilitator model
Bibliographic record
Abstract
Background : The demand for clinical placements and quality clinical supervision for nursing students remains an international issue. In light of on-going concerns related to recruitment and retention of nurses and constraints on fiscal resources both in education and healthcare, the aim of this review was to compare the “preceptor-facilitator” clinical supervision model used at a Magnet hospital against the “preceptor” and “facilitator” clinical supervision models frequently used in the clinical learning environment in Australia. Methods : A qualitative-descriptive review was undertaken using first, second and third year undergraduate nursing student evaluations (n = 93). Evaluations were completed during the last three days of students’ clinical placement using an electronic questionnaire. The questionnaire tool used open and closed questions to examine three main concepts: student-preceptor supervision; student-facilitation supervision; and the clinical learning environment. Findings : Three main themes emerged from the review. Firstly, how undergraduate nursing students highly valued the facilitator-student relationship; secondly, the importance of “support” as being an integral part of students’ clinical learning experience and thirdly; the recognition of students being considered as part of the health care team and a valued contributor towards patient care. Conclusion : The preceptor-facilitation model described and evaluated in this review offers an excellent clinical supervi- sion framework to support, nurture and foster a positive clinical learning environment for nursing students. This model of clinical supervision strategically aims not only to provide nursing students with high quality clinical supervision but offers nurses a supportive environment as preceptors and professional development opportunities through clinical facilitation. This review does highlight that there is a need for more research regarding the use of the preceptor-facilitator model to include the perspectives of both education and healthcare providers.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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 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".