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Record W3016678396 · doi:10.21037/jtd.2020.04.11

Women in thoracic surgery: social media and the value of mentorship

2021· review· en· W3016678396 on OpenAlexaff
Erin M. Corsini, Jessica G.Y. Luc, Mara B. Antonoff

Bibliographic record

VenueJournal of Thoracic Disease · 2021
Typereview
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMentorshipSocial mediaMedicineCardiothoracic surgeryRepresentation (politics)Value (mathematics)Medical educationPublic relationsSurgeryPolitical scienceWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

Social media serves as a tool to fill gaps in current efforts to promote women in cardiothoracic surgery, and, given its global reach, may be a particularly effective modality. Social media has an important role in networking and mentorship, especially for women seeking careers in specialties with relatively sparse female representation, such as cardiothoracic surgery. In addition, social media may facilitate professional interactions, collaboration, growth of online reputations, engagement in continued education, communication of novel research findings, and patient education. Herein, we review the evidence for social media in the networking and mentorship of women in cardiothoracic surgery. Future studies are needed to establish the durability of social media efforts and predictors in its effectiveness in achieving its goals.

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)0.015
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.964
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.090
GPT teacher head0.402
Teacher spread0.312 · 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 designOther design
Domainnot available
GenreReview

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

Citations34
Published2021
Admission routes1
Has abstractyes

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