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Record W4389219740 · doi:10.1182/blood-2023-182261

Unlocking Global Potential: Education and Funding Opportunities for International Trainees in Hematology

2023· article· en· W4389219740 on OpenAlexaff
Lourdes Gil-Flores, Emilia Ramos-Barrera, Andrea Flores-Díaz, Lillian Sung, David Gómez‐Almaguer, Andrés Gómez‐De León

Bibliographic record

VenueBlood · 2023
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsHematologyMedical educationHigher educationPolitical scienceMedicinePublic relationsInternal medicine

Abstract

fetched live from OpenAlex

Introduction: the practice of hematology is in continued transformation fueled by scientific advances. However, access to training and education is not equally distributed worldwide, especially for under-represented groups such as trainees living in low and middle-income countries (LMICs). Differences in education and funding in the field remain understudied. Our goal was to identify and review the different educational and funding opportunities offered by national and international hematology-focused academic societies. We aimed to provide an overview of the current state of continuing education in hematology and present gaps and areas of opportunity. Methods: We performed an online search to identify national and international hematological societies, including those targeted to a specific field. We extracted data regarding educational and funding opportunities in hematology by systematically evaluating each society's website from August 2022 until February 2023. We included all educational opportunities available that focus on hematology and are relevant for physicians and physicians in training. We abstracted the type of educational opportunity, if a membership to the society is required, cost, language, topic, target audience restrictions and their geographic region, and learning modality. We also included hematology-focused career enhancing grants or awards, such as travel stipends, research, or career development funding. Results: 25 hematology societies were included with 850 continuing medical education opportunities, consisting of 174 courses, 18 training programs, 10 self-assessments, 641 clinical cases, 4 books and 3 apps. We also included 55 grants and funding opportunities from 13 societies with a median of $45,000 USD (range, $330-600,000). More than half (61.8%) required a membership to apply, only 5 (9.1%) were available globally, and only 7 (12.7%) were designed for persons in LMICs. Most funding opportunities were targeted to postgraduate students (40%) while the smallest proportion were intended for undergraduate students (10.9%) (Figure 1). Most funding opportunities were for research and career development (47.3%), or for research and travel stipends (40%), 7 (12.7%) were for traveling. The median duration of funding was 1 year (range, 1 month - 4 years). Almost all courses (96%) and 61.1% of training programs were conducted online. Most courses (78.7%) were directed to all levels of preparation (undergraduates, postgraduates, and specialists), half of them (52.9%) required a membership and 43.1% were free for members. Educational opportunities spanned multiple topics (23.5%) or focused on classical or malignant hematology in a similar proportion (19.5 and 20.7%, respectively). Specialized education opportunities were more common for thrombosis (9.8%), pathology (6.9%), general professional skills (5.2%) transfusion medicine (2.3%), transplant (2.3%), and public health (1.7%). Of the 18 longitudinal training programs, 44.4% did not require a membership, and two thirds were free for members and non-members (66.7%). However, only 3 (16.7%) training programs were available globally, and the rest were exclusive for specific world regions: Europe and Latin America had 2 (11%) programs each, while Asia-Pacific, LMICs, and USA had 1 (5.6) each. Courses and programs findings are summarized in Table 1. Conclusion: Funding opportunities remain limited to trainees from LMICs. Most online courses require a membership which implies a cost, limiting education for international trainees. Efforts should be made to create more opportunities for underrepresented trainees.

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)0.010
metaresearch head score (Gemma)0.047
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: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.007
Science and technology studies0.0010.001
Scholarly communication0.0030.005
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.071
GPT teacher head0.355
Teacher spread0.283 · 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
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

Citations0
Published2023
Admission routes1
Has abstractyes

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