MétaCan
Menu
Back to cohort
Record W4388325995 · doi:10.1016/j.esmoop.2023.102058

On finding acceptance

2023· letter· en· W4388325995 on OpenAlexaff
David Chen

Bibliographic record

VenueESMO Open · 2023
Typeletter
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFeelingMentorshipPsychologyMedical educationWorkforceMedicineSocial psychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

‘Accepted’. This single word drowned out the vernacular in the rest of my acceptance letter to my dream MD/PhD program. In that moment, I was on cloud nine. After all, it was the culmination of both my research and clinical interests that will assuredly set my plan to train as a physician-scientist in oncology into motion. However, celebration soon transformed into empty emotion, and then into anxiety. Why did I feel so lost? To answer this question, my own reflections arrived at many of the same conclusions proposed by Lim et al. that contribute to attrition in the physician-scientist workforce.1Lim K.H. Westphalen C.B. Berghoff A.S. et al.Young oncologists’ perspective on the role and future of the clinician-scientist in oncology.ESMO Open. 2023; 8101625Abstract Full Text Full Text PDF Scopus (3) Google Scholar Beyond the personal, professional, institutional, and national/international factors indicated in this study, I wanted to highlight the often-overlooked but necessary role of mentorship to support the lifelong challenges of the physician-scientist pathway. My pursuit of the physician-scientist pathway was modeled after my own mentors who had completed training in MD/PhD programs. Each convinced me that I had what it takes to train as their future colleague during our intimate career discussions, and I fondly recall feelings of validation that came with my great respect for physician-scientists. I wanted to pursue MD/PhD training to one day fill their shoes. One week before the deadline to confirm my MD/PhD acceptance, I finally pieced together why I felt lost. The dream of MD/PhD training was not my own, but one ingrained by the well-intentioned recommendations of my mentors. When I considered bringing up my personal and practical considerations of this training pathway to my mentors, I expected a mix of shock, heartbreak, and even disrespect. It was none of the above. My mentors were unconditionally faithful and supportive so that I could positively make the right decision for myself. Given inadequate financial funding and my unclear prospects of securing a future position that combines research and clinical duties in the competitive research climate, I could not commit to the pursuit of the MD/PhD with full confidence. After deciding to discontinue my MD/PhD training, I found solace in knowing that my mentors successfully instilled in me the crux of the physician-scientist, regardless of my matriculation into an MD/PhD program—independent, critical thinking. Today, I remain interested in pursuing the physician-scientist training pathway, and find comfort and familiarity in doing so outside of a traditional MD/PhD program with the unconditional support of my research mentors for career and personal advice. As a mentee, we are often enclosed within a microcosm of mentors and find ourselves modeling after their every word due to our dependence for their career advice and letters of recommendation. Encouraging self-discovery by taking the path less travelled requires an unconditional level of faith that only strong mentor–mentee relationships embody. In parallel with our gratitude for our mentors’ support, mentees also owe it to ourselves to make personal decisions based on what feels right for us without fear of retribution for taking paths less travelled. As mentees, we all want to feel accepted in making our own unique decisions, and as future mentors, we should embrace when our mentees expect the same. The author thanks his mentors for their continued guidance and support. None declared.

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)0.001
metaresearch head score (Gemma)0.032
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, 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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.057
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.008

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 head0.392
GPT teacher head0.529
Teacher spread0.137 · 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
GenreCommentary

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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueESMO OpenSame topicHealth and Medical Research ImpactsFrench-language works237,207