275 PURE – a near-peer tutorship programme to improve paediatric medical education
Bibliographic record
Abstract
Objectives Near-peer teaching (NPT) is an approach to medical education that benefits both students and educators (1, 2). Paediatric trainees are expected to plan and deliver teaching (3) but lack structured opportunities and training to do so. Paediatric Undergraduate Regional Educators(PURE) programme was developed from a collaboration between three medical universities and a paediatric deanery to address these issues. The vision was to expose medical students to paediatric clinical teaching delivered by trainee doctors spread across 15 Trusts in the region. This abstract summarises the development of PURE and evaluates its efficacy. Methods A shared vision amongst three medical schools from three universities in East of England(EoE) region led to creation of PURE in collaboration with the region’s School of Paediatrics(SOP). In the first Pilot project(2021), 11 Paediatric Educators (PEs) across 4 Trusts delivered teaching to approximately 150 students. The students were taught a universal curriculum agreed between the medical schools. The educators were trained over two training evenings by senior faculty members from the universities and personnel from the SOP deanery, with one-to-one mentorship, administrative support and educational resources. The pilot project expanded in 2022 to include 17 PEs across 8 trusts teaching 250 students Results 77/150 students and 10/11 PEs responded to a feedback survey after the pilot project. The learning outcomes covered were respiratory system examination (67.5% students), development assessment (46.8%) and examination of nervous system (48.1%). 100% PEs reported that they received one-to-one mentorship, and 60% were able to have peer-review of their teaching. 40% PEs felt extremely confident to deliver bedside teaching after participating in PURE as compared to 9.1% before PURE. 100% PEs said they would teach within PURE again. 81.8% of medical students reported that the content taught was extremely relevant to their needs while 92% responded that sessions were delivered as scheduled. Conclusions This novel collaboration between 3 universities and a postgraduate deanery has benefitted both students and trainees, delivering accessible and highly-regarded bedside teaching to medical students and providing avenue for them to develop interest in paediatrics which links with the RCPCH initiative #choosepaediatrics whilst enabling trainees meeting curriculum competencies and developing teaching skills (4). Going forward, PURE envisions a self-sustained Team model that is based upon focused recruitment, with a parallel and supportive mentorship program for PEs, aspiring to reach 500 medical students every year and improving the quality, uniformity, and accessibility of undergraduate clinical teaching in paediatrics. References Can Near Peer Teaching Improve Academic Performance. International Journal of Higher Education. Williams. 2014. Khapre M, Deol R, Sharma A, Badyal D. Near-Peer Tutor: A Solution For Quality Medical Education in Faculty Constraint Setting. Cureus. 2021. GMC Approved Paediatric Postgraduate Curriculum. https://www.gmc-uk.org/education/standards-guidance-and-curricula/curricula/paediatrics-curriculum, 2018. Introducing Our New Choose Paediatrics Programme. https://www.rcpch.ac.uk/news-events/news/introducing-new-choose-paediatrics-careers-programme. 2021.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".