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Record W4414208611 · doi:10.1192/j.eurpsy.2025.921

PsychED – A National Teaching Programme to Boost Medical Student Engagement in Psychiatry

2025· article· en· W4414208611 on OpenAlexaff
R. Hafes, Deepti Varma, Neville A. Stanton, S. Thakaria

Bibliographic record

VenueEuropean Psychiatry · 2025
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsSession (web analytics)Likert scaleMental healthCore competencyPresentation (obstetrics)Inclusion (mineral)

Abstract

fetched live from OpenAlex

Introduction In the 2019 Choose Psychiatry guidance for medical schools in the UK, medical students voiced a need for more extracurricular activities focused on mental health. They specifically requested the inclusion of psychiatric clinicians in their education to deepen their understanding of psychiatry and its different roles. Applications for core psychiatry training have risen dramatically, leading to oversubscription in recent years. In response to this demand, PsychED UK , a national teaching programme was created to further foster students’ knowledge and interest in psychiatry. Objectives - Increase medical students’ engagement in psychiatry as a career. - Enhance students’ knowledge and confidence in psychiatric practice. - Boost interest in psychiatry through practical insights and exposure. Methods Between December 2023 and February 2024, six one-hour teaching sessions were offered to medical students and Foundation Programme trainees through Medall, a virtual healthcare platform, delivered by consultants, registrars, and core trainees, ensuring a broad range of experiences. In order to increase engagement we selected different types of presentation content: session 1 and session 4 focused on practical and personal tips on life as a psychiatry trainee, session 2 and session 3 delivered high-yield content tailored to support exam revision and lastly session 5 and session 6 gave an insight into subspecialties, providing a glimpse into exciting career pathways beyond the general practice. Pre- and post-session questionnaires were used to measure the satisfaction scores, including likert scale ratings focusing on the student’s knowledge, confidence, and interest in a career in psychiatry and free text box for qualatative data. Results We had a strong turnout, with 126 attendees participating across six sessions. The audience included a diverse range of individuals, from first-year medical students to Foundation Year 2 trainees, with participants joining from across the UK and even internationally. The results showed strong overall satisfaction, with all sessions scoring 77% or higher. Sessions 1, 3, and 4 received the highest satisfaction ratings, and Sessions 1 and 4 had the highest percentage of attendees providing feedback, indicating strong engagement with the content. Free-text feedback analysis highlighted that attendees were seeking more personal experiences and insights from current core trainees. Image 1: Image 2: Conclusions The PsychED UK programme effectively enhanced medical students’ knowledge and confidence in psychiatry, addressing key areas identified in previous student feedback. Based on these positive results, a second series of teaching sessions is planned, with further refinements to the programme. Due to the high level of engagement and satisfaction with sessions 1 and 4, the second cycle will be presented by two speakers in a conversational format, focusing on the experiences of psychiatry trainees. Disclosure of Interest None Declared

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.400
Teacher spread0.379 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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