Review of Models of Employment for Nursing Roles which Bridge Practice and Education:A Report for NHS Education for \nScotland
Bibliographic record
Abstract
This review was conducted for NHS Education for Scotland. It investigates the literature pertaining to different models of contractual employment for nurses that ‘bridge’ practice and education in order to identify the evidence base and current practice in other countries. The specific context for the review was the development of clinical education career pathways in Scotland as part of Modernising Nursing Careers (MNC) initiative. The objectives were to review the literature on evaluation, and on reported strengths and weakness of different models of employment/deployment of nurses in practice/education roles, to provide a typology of the key characteristics of these different roles – where possible to include job \ndescriptions, types of contractual employment (e.g. fixed term, open ended, joint appointment, sessional etc.); employment status; work location(s) etc. and to highlight examples of such roles in different health systems. \nAreas explored in the review included models of employment, career structure, and role content of nurses in these roles. In practice the review of published material highlighted that the literature did not enable all the \nobjectives to be met in detail- in particular there is little published evidence on types of employment contracts etc. This may be because much of the publicly available literature is written from an educational delivery perspective rather than from a workforce/HR perspective. Supplemental information on this issue was obtained from contacts in other countries- notably Canada and New \nZealand- to provide relevant background information. \nThe Practice Education Facilitator (PEF) is one recent NHS Scotland role which aims to support mentors and mentorship in the clinical areas, helping develop and promote those areas as learning environments (NES, 2007). The \nPEF is just one type of role that bridges between practice and education. The types and roles of personnel used to bridge the ‘practice-education gap’, or ‘practice-theory gap’, their places of practice, and conditions of employment \nvary widely throughout the world. This review examines some of these roles, with the objective of examining strengths and weaknesses associated with different models of employment/deployment, with a view to informing NHS \nEducation for Scotland (NES).
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".