MétaCan
Menu
Back to cohort
Record W4296833734 · doi:10.3390/genes13101707

Rare Disease Education Outside of the Classroom and Clinic: Evaluation of the RARE Compassion Program for Undergraduate Medical Students

2022· article· en· W4296833734 on OpenAlexaff
Ari Morgenthau, Colton Margus, Michael P. Mackley, Ashley Miller

Bibliographic record

VenueGenes · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsSickKids FoundationDalhousie UniversityHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsCompassionAppealMedical educationPsychologyMedicineFamily medicinePolitical science

Abstract

fetched live from OpenAlex

Launched in 2014, the RARE Compassion Program is the first international educational program to pair medical students with rare disease patients in order to enhance exposure to and comfort with rare diseases. As part of ongoing quality improvement, this study retrospectively reviewed four years of participant registration data to conduct a program evaluation of the RARE Compassion Program between 2014–2018. During the study period, there were 334 student participants, representing 67.3% of Association of American Medical Colleges (AAMC) member medical schools, and 5389 rare disease volunteers. Despite not requiring in-person interaction, 90.64% of student–volunteer interactions were in-person, while only 5.89% and 3.46% were by video messaging or email correspondence, respectively (p = 0.0002). In a limited post participation survey, 91.7% of students, who matched to 19 out of 27 residency specialities, indicated they would recommend the program to their peers. These findings suggest that the RARE Compassion Program, designed to increase medical student engagement with rare disease patients, has broad appeal. It serves as a novel case study of how extracurricular initiatives supported by non-profit organizations can augment the medical training experience and improve understanding of important and often neglected perspectives.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.270
Threshold uncertainty score0.152

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.352
Teacher spread0.331 · 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".

Quick stats

Citations11
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueGenesSame topicGenomics and Rare DiseasesFrench-language works237,207