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Record W7064507652

Career Mentors & 5-Year Data on the IUSM Anesthesiology Match

2023· other· en· W7064507652 on OpenAlexfundno aff

Bibliographic record

VenueIUScholarWorks (Indiana University) · 2023
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersYork UniversityUniversity of PittsburghHarvard UniversityVanderbilt University
KeywordsAnesthesiologyMentorshipGraduation (instrument)Class (philosophy)AppealPresentation (obstetrics)Medical school
DOInot available

Abstract

fetched live from OpenAlex

Introduction:
\nThe IU Department of Anesthesia provides Anesthesiology Career Mentors to 3rd and 4th year medical students. We have approximately 47 requests per class year. In the past 5 years, we have matched 181 students into Anesthesiology, averaging 36 students per year (range 30-46).
\nWhere do these students match into Anesthesiology? How many of them use the Career Mentorship program? If they don’t choose Anesthesiology, which other specialties appeal to them?
\n
\nObjective: 
\nThe purpose of this presentation is to examine the pipeline of students interested in anesthesiology who request a career mentor and match into anesthesiology. 
\n
\nMethods: 
\nMatch data from publicly obtained IUSM Graduation Booklets for the Class of 2017 through 2021 was filtered for those students matching into Anesthesiology Residency Programs. These programs were mapped and cross-referenced for medical school rankings based on the 2022 US News & World Report Medical School Rankings for Research. 
\n
\nAnesthesiology career mentorship requests were tracked starting in 2019 for the class of 2020 onwards, so students who matched into Anesthesiology were cross-referenced with students who had formally requested Anesthesiology Career Mentors in 2020 and 2021. Students who had formally requested Anesthesiology Career Mentors in 2020 and 2021 were also cross-referenced with the IUSM Graduation Booklet data to see how many of these students matched into Anesthesiology or other fields. 
\n
\nResults: 
\nOf the 181 students that have matched into Anesthesiology from 2017-2021, 63 students matched at IU (35%). The rest are distributed across the regions of the US, including residency programs at the top 25 medical schools including Harvard, NYU, Duke, Stanford, and UCSF. 
\n
\nThe majority of students matching into Anesthesiology request Anesthesiology Career Mentors, with 73% (48/66) of students assigned to mentors in the graduating class of 2020 and 2021. 96 mentors were requested in the class of 2020 and 2021, with 29 students (30%) not matching into Anesthesiology. These students may have changed careers and not applied to Anesthesiology. Many of these students choose to pursue other specialties, including Internal Medicine, Radiology, Pediatrics, Family Medicine, General Surgery, Obstetrics-Gynecology, Orthopedic Surgery, and Psychiatry. Some students were not listed in the IUSM Graduation Booklet or did not have a residency listed. 
\n
\nConclusion: 
\nLimitations of this analysis include students’ choice to publish their Match data in the IUSM Graduation Booklet and the possibility of some students being lost to follow up due to not graduating yet or changing their name. No direct link can be made between formally assigned Anesthesiology Career Mentors and the Match, especially since some students may have sought out informal mentorship. 
\n
\nAnesthesiology continues to be a competitive field with high student interest. More research can be done to understand factors that influence student decisions for specialty and to track student alumni and follow their career progression into fellowship and the physician workforce. Additional data collection on the usefulness of the career mentorship program and ways to improve and further support student career choice and Match success will be especially helpful as Step 1 changes to pass/fail.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.265
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.2690.004

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 head0.044
GPT teacher head0.252
Teacher spread0.208 · 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; both teacher heads agree on what is shown here.

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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