Learner-Led Nursing Placements in England: Evaluating Impact and Implementation Opportunities
Bibliographic record
Abstract
Introduction: ‘Learner-led’ placement models in nursing, such as ‘Collaborative Learning in Practice’ and coaching seek to improve learner experience, competence, and retention as well as increasing placement capacity (Hill et al 2020). Whilst there is appetite within NHS Trusts to adopt learner-led models, there are gaps in understanding how best to do this.Aim: To provide insight into the organisational and socio-cultural conditions needed for successful implementation of learner-led placements.Methods: Mixed methods evaluation, underpinned by Appreciative Enquiry (Whitney & Cooperrider 2011) and Kirkpatrick’s Evaluation Framework(Kirkpatrick 1998), commissioned in 2024 by NHS England. Work-packages included i) scoping review; ii) five organisational case studies; iii) semi-structured interviews with nurses, students and academics. Qualitative data analysed thematically (Braun & Clark 2022). (CU Ethics Ref P183633, 23/01/2025)Results: The scoping review included 32 papers and case studies yielded 52 interviews. Learner-led typologies include; collaborative models; coaching approaches; learner-led dyads, triads, clinics; and interprofessional approaches. Student benefits derived from learner led models can be framed using Self Determination Theory (Deci & Ryan 2000). Nurses were pro-active in implementing early interventions to prepare students for the transition to registration . Contextual factors within the learning milieu, including the culture; nursing team maturity; and acuity and activity levels, impacted learning. The prevailing perception was that learner-led models resulted in better patient care.Discussion: Based on evaluation findings, methods of embedding and optimising learner-led placements are presented. Rather than a ‘one-size-fits-all’ approach, case study sites adapted typologies along a continuum to suit the clinical context and varying student needs. Learner-led placements can contribute to improved outcomes for student, patient and nurses.Conclusion: Understanding typical barriers and facilitators to learner-led placements provides a framework for successful implementation. Continued evaluation and adaptation of learner-led models will be crucial in responding to the evolving needs of healthcare education.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".