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Record W4352991618 · doi:10.1177/23821205231164029

Scholarly Opportunities for Medical Students and Residents in Canadian Medical Professional Organizations

2023· article· en· W4352991618 on OpenAlexafffundabout
Mitchell D. Thatcher, Adam Michael Wandzura, Mckinley C Smith

Bibliographic record

VenueJournal of Medical Education and Curricular Development · 2023
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Association of Thoracic Surgeons
KeywordsMentorshipSpecialtyMedical educationMedicinePublic relationsPolitical scienceFamily medicine

Abstract

fetched live from OpenAlex

Objectives: Participation in medical specialty organizations can provide medical students and residents with additional research, advocacy, networking, and leadership opportunities. Although past research has looked at individual specialties in the United States, little is known about trainee involvement in Canadian organizations. Therefore, the aim of this study is to review the opportunities available for medical students and residents within Canadian medical specialty organizations. Methods: The websites of 71 Canadian medical specialty organizations were reviewed to assess levels of trainee participation. Results: Of the 71 organizations reviewed, 42 (59%) allow medical students and 67 (94%) allow residents to become members. Most organizations allow trainees to attend their annual conference (83% for students and 93% for residents), and the mean cost of attending the most recent virtual conference was $114 (range: $0-$475) for students and $142 (range: $0-$475) for residents. Twenty-two organizations (31%) have travel awards for students and 37 (52%) have awards for residents. Research grants are available in 41 (58%) of organizations for students and 56 (79%) for residents. Formal mentorship programs exist in 16 (23%) organizations for students and 25 (35%) for residents. Conclusion: To our knowledge, this study highlights for the first time the scholarly opportunities available to trainees within Canadian medical specialty organizations.

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)0.004
metaresearch head score (Gemma)0.014
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.649

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0050.001
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.110
GPT teacher head0.479
Teacher spread0.369 · 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

Citations0
Published2023
Admission routes3
Has abstractyes

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