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Record W3139514025 · doi:10.1016/j.adro.2021.100686

Remote Mentorship in Radiation Oncology: Lessons to Share

2021· article· en· W3139514025 on OpenAlexaff
Crystal Seldon, W. Wong, Reshma Jagsi, Jennifer Croke, Anna Lee, Lindsay Puckett

Bibliographic record

VenueAdvances in Radiation Oncology · 2021
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMentorshipRadiation oncologyMedicineScopusOncologyMedical educationUnderrepresented MinorityClinical OncologyInternal medicineFamily medicineMEDLINERadiation therapy

Abstract

fetched live from OpenAlex

Mentorship is a key factor in promoting and maintaining fulfillment in medical practice. Invariably, physician success stories usually have a common thread: an important mentor, or mentors, whose guidance proved invaluable. Finding mentors has been noted as a challenge for women in radiation oncology given low representation in the field.1 In 2019, women comprised only 17.4% of department chairs and program directors and 30.7% of faculty.2 Digital, or remote, mentorship seems an ideal solution to connect women mentors and mentees, especially given findings that over a quarter of female residents train in programs with less than or equal to 2 female faculty.

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)0.022
metaresearch head score (Gemma)0.065
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: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.065
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.002
Science and technology studies0.0040.005
Scholarly communication0.0110.025
Open science0.0040.013
Research integrity0.0100.016
Insufficient payload (model declined to judge)0.0220.008

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 head0.048
GPT teacher head0.441
Teacher spread0.394 · 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
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

Citations13
Published2021
Admission routes1
Has abstractyes

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