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Record W3092583201 · doi:10.1186/s12909-020-02270-7

Reference letters for subspecialty medicine residency positions: are they valuable for decision-making? Results from a Canadian study

2020· article· en· W3092583201 on OpenAlexafffundabout
Deepti Chopra, M. G. Joneja, Gurjit Sandhu, Christopher A. Smith, Catherine M. Spagnuolo, Lawrence Hookey

Bibliographic record

VenueBMC Medical Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsKingston Health Sciences CentreHotel Dieu HospitalKingston General HospitalQueen's University
FundersSoutheastern Ontario Academic Medical Organization
KeywordsSubspecialtyProgram directorSpecialtyAuditMedical educationPsychologyQuality (philosophy)Sample (material)MedicineFamily medicineManagement

Abstract

fetched live from OpenAlex

BACKGROUND: The letter of recommendation is currently an integral part of applicant selection for residency programs. Internal medicine residents will spend much time and expense completing sub-specialty away electives to obtain a letter of recommendation. The purpose of this study was 1) to examine a large sample of reference letters in order to define essential components of a high-quality letter, and 2) to elucidate the relationship between quality of reference letter and the letter writer. METHODS: We conducted a two-phase study. In phase one, a large sample of letters of recommendation was examined using an audit tool as a coding framework. A 5-point composite endpoint of high-quality letter components was subsequently developed. In phase two, program director letters were compared to non-program director home institution and non-home institution elective letters based on inclusion of components of the 5-point composite endpoint using Chi square testing. RESULTS: 715 letters were examined (398 non-program director home institution letters, 201 program director letters, and 116 non-home institution elective letters). High-quality letter components were: nature of relationship, duration of relationship, In Training Evaluation Report information, research involvement and comments on areas for improvement. Program director letters had a significantly higher proportion (10.4%) of all 5 high-quality components, compared to 0% in both non-program director home institution letters and elective letters (p < 0.001). A significantly higher proportion of program director letters had 4-5 high-quality components (62.5%) compared to 2% of non-program director home institution letters and 0% of elective letters (p < 0.0001). CONCLUSIONS: Letters of recommendation from elective rotations are of the poorest quality and such rotations should not be pursued for the sole purpose of obtaining a letter. The low quality of elective letters leads to the recommendation that writers should decline to write them, programs should not require them and trainees should not request them. Program directors write the highest quality letters and should be a resource for faculty development. Clinical supervisors can use the 5-point composite endpoint as a guide when writing letters for applicants.

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)0.003
metaresearch head score (Gemma)0.063
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.687
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.063
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.090
GPT teacher head0.393
Teacher spread0.303 · 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.

Study designNot applicable
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

Citations4
Published2020
Admission routes3
Has abstractyes

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