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Record W3014785943 · doi:10.29392/001c.12223

Academic careers in global pulmonary and critical care medicine: perspectives from experts in the field

2020· article· en· W3014785943 on OpenAlexaff
Alfred Papali, Janet Dı́az, E Jane Carter, Juliana Carvalho Ferreira, Rob Fowler, Tewodros Haile Gebremariam, Stephen B. Gordon, Burton W. Lee, Srinivas Murthy, Elisabeth D. Riviello, T. Eoin West, Neill K. J. Adhikari

Bibliographic record

VenueJournal of Global Health Reports · 2020
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversity of British ColumbiaHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsCareer PathwaysMedical educationGlobal healthMedicineProfessional developmentExperiential learningEngineering ethicsHealth carePolitical scienceNursingPsychologyPedagogyPublic healthEngineering

Abstract

fetched live from OpenAlex

Academic global pulmonary/critical care medicine (PCCM) remains a relatively novel concept not fully embraced by all training programs, so PCCM early-career professionals may have little guidance in building successful careers in this field. To highlight various approaches used by current PCCM faculty to incorporate global health into their academic careers, speakers from a global health careers mini symposia held at the 2017 and 2018 American Thoracic Society International Conferences were invited to submit perspectives reflecting on academic PCCM and global health. The collection of essays was collated into a single manuscript. Eight current global PCCM faculty from diverse geographic and professional backgrounds provide experiential guidance for early-career professionals interested in global academic PCCM. Trainees and junior faculty interested in academic global PCCM will find innumerable obstacles to developing this non-traditional career pathway, but there exist diverse pathways to success.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 science0.0000.000
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 head0.028
GPT teacher head0.409
Teacher spread0.380 · 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 designObservational
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

Citations3
Published2020
Admission routes1
Has abstractyes

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