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Record W2783125661 · doi:10.1002/rth2.12070

Early career professionals: A challenging road

2018· article· en· W2783125661 on OpenAlexaff
Ketan Kulkarni, Joshua Muia, Yacine Boulaftali, Marc Blondon, Mandy N. Lauw

Bibliographic record

VenueResearch and Practice in Thrombosis and Haemostasis · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsIzaak Walton Killam Health Centre
Fundersnot available
KeywordsWork (physics)Face (sociological concept)Human multitaskingBurnoutPublic relationsMedical educationCareer developmentPsychologyMedicinePolitical scienceEngineering ethicsSociologyEngineering

Abstract

fetched live from OpenAlex

The term "early career professional" (ECP) indicates a generation of professionals that demonstrates the potential to succeed established professionals and leaders. The field of thrombosis and hemostasis has a large ECP community. They often face typical challenges in their endeavor to establish an independent academic career. A healthy work‐life balance and mentoring are key for ECPs to create the ideal circumstances to advance their careers. However, multitasking training, research and clinical duties is a burden, and burnout is a looming issue. The advent of the smartphone presents an additional challenge by blurring the border between work and non‐work time. This Forum describes some of the typical challenges faced by ECPs, highlights existing literature and suggests practical solutions relevant to ECPs in the field of thrombosis and hemostasis, and the possible role of the ISTH Early Career Committee.

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)0.034
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.179

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.046
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0310.014
Scholarly communication0.0250.030
Open science0.0030.030
Research integrity0.0200.030
Insufficient payload (model declined to judge)0.0270.008

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 head0.345
GPT teacher head0.510
Teacher spread0.165 · 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.

Study designNot applicable
DomainIncentives
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

Citations11
Published2018
Admission routes1
Has abstractyes

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