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Record W4410014575 · doi:10.1016/j.fhj.2025.100252

Integrating multimorbidity education into undergraduate medical curriculum: A systematic review

2025· review· en· W4410014575 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFuture Healthcare Journal · 2025
Typereview
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsWestern University
Fundersnot available
KeywordsCurriculumMultimorbidityMedical educationMedicineEngineering ethicsPedagogyPsychologyEngineeringComorbidityPathology

Abstract

fetched live from OpenAlex

Multimorbidity is becoming highly prevalent across the globe. Current medical curricula predominantly focus on single-disease management, leaving future physicians underprepared to provide patient-centred care to address the multimorbidity epidemic. Through a systematic review, this short communication identified the current state of multimorbidity education in medical curricula and evaluated existing educational approaches. A narrative systematic review (CRD42024585500) of studies meeting stringent inclusion criteria focusing on multimorbidity education was conducted. The review found limited evidence regarding the integration and effectiveness of current educational approaches. Although multimorbidity education was associated with improved self-reported student confidence, there was a notable absence of structured, formal teaching frameworks, with education primarily occurring through informal clinical exposure. This systematic review discovered the absence of an evidence base on structured, validated teaching methods that are urgently needed to better prepare future physicians to manage patients with multimorbidity effectively.

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.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.356
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
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 head0.037
GPT teacher head0.450
Teacher spread0.413 · 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