Letter to the Editor: Best Practices on Public and Patient Involvement in Interprofessional Healthcare Education
Bibliographic record
Abstract
We read with interest the article ‘Public Participation in Healthcare Student Education: An Umbrella Review’ by Nowell et al. [1]. Their work highlights the benefits of involving patients in healthcare education, such as enhancing empathy, patient-centred decision-making and safety [1]. The PULPIT Consortium, funded by ERASMUS+, promotes public and patient involvement (PPI) in the interprofessional education (IPE) of healthcare students [2]. Our project addresses students' limited early patient interaction and poor understanding of patient-centred care and healthcare roles. We aim to implement an educational module that will be freely accessible through a dedicated online platform, as well as recommendations for PPI in the IPE of undergraduate healthcare students, following the ‘Vancouver Statement’ [3]. This project builds upon two main partner initiatives: the ‘Health Mentors Programme’, coordinated by the Patient and Community Partnership for Education (PCPE; https://health.ubc.ca/pcpe), and the ‘Patient as a Person’ project [4], developed by the Maastricht University and the Patient as a Person Foundation (https://mensachterdepatient.nl/), which have been instrumental in advancing patient involvement in healthcare education. Nowell et al.'s article [1] is a valuable resource for raising awareness of the benefits and complexities of PPI in healthcare education. Despite the wealth of research on the benefits, authentic patient involvement in IPE remains a blind spot. We challenge interprofessional educators to involve ‘Experts by Experience’ (EBEs) in all aspects of IPE (curriculum design, delivery, research and evaluation) so that students can learn how to collaborate with the public and patients as equal and valued members of the healthcare team. It is long overdue and is the core purpose of the PULPIT Consortium. Ricardo J. O. Ferreira: conceptualization, supervision, writing–original draft, funding acquisition. Matilde Leal: writing–original draft, project administration. Elsa Frazão Mateus: writing–review and editing. Lucija Gosak: writing–review and editing. Matthijs H. Bosveld: conceptualization, writing–review and editing. Cathy C. Kline: conceptualization, writing–review and editing. The members of the PULPIT Consortium include the following: Cristina Baixinho, Adriana Henriques, Andreia Silva Costa, Paulo Costa (Escola Superior de Enfermagem de Lisboa, Lisbon, Portugal); Catarina Lima, Pedro Morgado, Nadine Santos (Escola de Medicina da Universidade do Minho, Braga, Portugal); Dante Mulder, Sjim Romme (Stichting Mens achter de Patiënt, Eijsden, The Netherlands); Koen Goffings, Bruno Van Koeckhoven (Hogeschool PXL, Hasselt, Belgium); Barbara Kegl, Mateja Lorber (Univerza v Mariboru, Maribor, Slovenia); Danielle Derijcke, Mitchell Silva (EUPATI Belgium, Belgium); Angela Towle (University of British Columbia, Vancouver, Canada); Khadidja Abdallah, Isabelle Huys, Charlotte Verbeke (Katholieke Universiteit Leuven, Leuven, Belgium). This study was funded by Erasmus+ (Grant Number: 2023-1-PT01-KA220-HED-000165015). The authors declare no conflicts of interest. The authors have nothing to report.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".