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Record W2922868688 · doi:10.36834/cmej.44141

Murmurs: the journal of art and healing

2019· article· en· W2922868688 on OpenAlexaffvenue
Marlon Danilewitz, Andrea Zumrova, Lynn Z. Bloom, Farriss Blaskovits

Bibliographic record

VenueCanadian Medical Education Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsUniversity of OttawaUniversity of British Columbia
Fundersnot available
KeywordsDisconnectionCreativityResilience (materials science)PublishingPsychological resiliencePsychologyMedical educationMedicinePolitical scienceSocial psychologyLaw

Abstract

fetched live from OpenAlex

In an era of unprecedented physician burnout and disconnection, there is growing recognition of the importance of integrating the study of humanities in medicine. Emerging research has underscored the importance of reflective writing and creativity in bolstering physician resilience. The publication “Murmurs Magazine,” therefore, should be of great interest to medical educators. This publication was developed and continues to be managed by medical students as a forum for creative expression and reflection.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.048
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0060.007
Scholarly communication0.0160.003
Open science0.0010.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0480.008

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 head0.010
GPT teacher head0.300
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2019
Admission routes2
Has abstractyes

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