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Record W4415376278 · doi:10.1186/s12909-025-07142-6

Medical education on inpatient medical oncology service before and after oncology hospitalist program

2025· article· en· W4415376278 on OpenAlexaff
Ronald Chow, Candice Y. Kaminski, Hadrian Mendoza, Joshua Rusheen, Nathaniel Parker, Shayna Schor, Jaya Gupta, Anna Jaiani, Jensa C. Morris, Elizabeth Pršić

Bibliographic record

VenueBMC Medical Education · 2025
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMentorshipService (business)Work (physics)MEDLINESurgical oncologyGraduate medical educationMedical school

Abstract

fetched live from OpenAlex

BACKGROUND: At Smilow Cancer Hospital, internal medicine-boarded oncology hospitalists assumed primary team attending responsibilities on an adult inpatient medical oncology academic service beginning in 2021. Medical oncology faculty transitioned from primary team attending to consulting physicians but remained engaged in daily morning teaching and twice-weekly formal afternoon didactics. The aim of this study was to compare the educational experiences of internal medicine residents before and after implementation of the oncology hospitalist program. METHODS: Yale School of Medicine internal medicine residents receive MedHub surveys following the inpatient oncology rotation. Surveys completed before July 2021 were compared to those after July 2022, when oncology hospitalists were fully integrated into both of the medical oncology teaching services. Surveys asked residents to: (1) score their rotation experience on a 5-point Likert scale (Poor = 1, Good = 2, Very Good = 3, Fair = 4, Excellent = 5) and (2) delineate major positives and negatives noted on their rotation using a free-text field. Answers were compared across timepoints using parametric tests. RESULTS: 118 participants completed the survey pre-implementation, and 84 completed the survey post-implementation. Residents completing the survey post-implementation reported greater general satisfaction (p = 0.005), greater balance between education and clinical demands (p = 0.019), improved resident education (p = 0.027), and greater hospitalist support (p < 0.001). There was greater operational challenges post-implementation (p = 0.003). DISCUSSION: Previously-published literature has reported oncology hospitalist programs to have good hospital outcomes, with satisfaction and acceptance by oncologists. This article adds to the literature, suggesting that residents also experience improved clinical experience and support as well as a greater balance between clinical and educational demands. Further work can build upon the hospitalist model and explore oncology-specific education and mentorship to supplement the existing educational experience.

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)0.012
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.856
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.009
GPT teacher head0.381
Teacher spread0.372 · 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 designOther design
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

Citations0
Published2025
Admission routes1
Has abstractyes

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