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Record W4413369537 · doi:10.4300/jgme-d-24-00791.1

Perceptions of an Inter-Residency Narrative Medicine Curriculum

2025· article· en· W4413369537 on OpenAlexaff
Molly Zeme, Sara Buckelew, Anoushka Sinha

Bibliographic record

VenueJournal of Graduate Medical Education · 2025
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsResidency trainingCurriculumMedical educationNarrativeMedicineGraduate medical educationMEDLINEPerceptionData sciencePsychologyComputer scienceAccreditationPedagogyBiology

Abstract

fetched live from OpenAlex

ABSTRACT Background Narrative medicine (NM) is a discipline that equips clinicians with essential skills such as close reading, observation, and reflective practice, enabling them to listen more attentively and gain a deeper understanding of health care. Despite these advantages, many training programs lack skilled facilitators to implement NM locally. To address this gap, a hybrid virtual/in-person inter-residency NM curriculum was developed. Objective This study aims to explore residents’ perceptions of the NM curriculum and how they describe its influence on their personal or professional development. Methods We conducted 2 focus group interviews of pediatrics residents who attended at least one NM workshop. The 50-minute workshops occurred monthly from July 2023 to July 2024 as part of the mandatory pediatrics residency conference curriculum, though attendance was secondary to clinical duties. Attendance included 10 to 15 residents and 1 to 2 fellow or faculty facilitators. Interviews were recorded, transcribed, and thematically analyzed. Results Through analysis of 2 focus groups comprising 13 total residents, we identified 3 themes: reconnecting with humanity through reflection, strengthening relationships with colleagues and patients, and appreciating institutional support for NM. Conclusions Our study demonstrates that an inter-residency NM curriculum fostered a sense of community by enabling residents to reflect on their clinical practice and connect with colleagues within and beyond their respective campuses. Residents valued the opportunity to learn from peers and supervisors alike and appreciated program leadership’s support of NM.

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)0.001
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
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)0.0010.000

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 head0.024
GPT teacher head0.404
Teacher spread0.380 · 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.

Study designQualitative
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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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