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Record W3209903772 · doi:10.1093/pch/pxab061.011

15 2020 CaRMS Residency Match Confirms Popularity of Pediatrics

2021· article· en· W3209903772 on OpenAlexaffabout
Anne Rowan-Legg, Marc Zucker

Bibliographic record

VenuePaediatrics & Child Health · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsSpecialtyMedicineMatching (statistics)Family medicinePediatricsPopularityMedical educationMultiple choiceGraduate medical educationMedical schoolPsychologySignificant differenceInternal medicine

Abstract

fetched live from OpenAlex

Abstract Primary Subject area Medical Education Background Longitudinal data about the interest in, and competitiveness of, pediatric postgraduate training in Canada has not been reported. Objectives 1. To describe the results of the 2020 CaRMS pediatric residency match with respect to application rates, first-choice discipline choices, and succesful match rates by gender. 2. To examine the trend of these indices over the past decade. Design/Methods Data from the 2020 Canadian Residency Matching Service (CaRMS) pediatric residency match was evaluated and compared over the past decade. Residency match data from other programs was also used for some comparison reporting. Results Of a total pool of 2998 Canadian medical graduate (CMG) applicants in 2020, 305 (10.2%) applied to pediatrics, and 17 of these latter applicants (5.6%) applied solely to pediatrics. In the first iteration CaRMS match, pediatrics was the first-choice discipline for 177 CMG applicants (6.0% of all first choices). Pediatrics has been consistent as a first-choice discipline over the years: 5.9% (2017), 5.5% (2015), and 6.1% (2013). Of the 155 first-year positions offered in pediatrics this year, all were filled. Of those CMGs who matched to pediatrics in 2020, the specialty was the first-choice discipline for 128 applicants (92.8%) and the second-choice discipline for 9 applicants (6.5%). There were clear gender differences noted. Pediatrics accounted for 8.3% of female and 3.2% of male first-choice disciplines. Of the 135 females whose first-choice discipline was pediatrics, 101 matched to that first choice (74.8%). Of the 41 males whose first-choice discipline was pediatrics, 26 matched to that first choice (63.4%). Since 1995 (at CaRMS’ inception), the rates of first-choice discipline choice by gender have been quite stable (Table 1), with females consistently higher than males, while the first-choice discipline matching rate by gender have varied (Figure 1). Forty CMG applicants whose first-choice discipline was pediatrics matched to an alternate discipline choice and nine went unmatched, suggesting that pediatrics continues to be a competitive discipline. The pediatric rate of first-choice discipline matching to another alternate choice of 22.6% (40/177) is comparable to Anesthesia (22.1%; 34/154), Ophthalmology (26.7%; 20/75), and Otolaryngology (20.9%; 9/43). Conclusion Pediatrics continues to be a top specialty choice for graduates of Canadian medical schools, according to data from the 2020 CaRMS match. There are gender differences noted in the choice of pediatrics as a first-choice discipline, and in the successful match rate to pediatrics programs. The rate of successful first-choice discipline matching by gender have varied over time, with the past two years showing significantly greater matching success for females. These trends in the CaRMS pediatric data have implications on discipline recruitment and the pediatric workforce in Canada, and merit further exploration.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.995
Threshold uncertainty score0.965

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.002

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 head0.022
GPT teacher head0.306
Teacher spread0.284 · 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 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".

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Citations0
Published2021
Admission routes2
Has abstractyes

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