ArticleJournal of graduate medical education2020
Everyone Is Awesome: Analyzing Letters of Reference in a General Surgery Residency Selection Process.
Article in Journal of graduate medical education, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Reading between the lines: exploring the unwritten rules of letters of recommendation in the Canadian resident selection process.Canadian medical education journal · 2024Article
- A scoping review of Fit in medical education: a guaranteed success, or a threat to inclusivity?Canadian medical education journal · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe resident selection process involves the analysis of multiple data points, including letters of reference (LORs), which are inherently subjective in nature.
objectiveWe assessed the frequency with which LORs use quantitative terms to describe applicants and to assess whether the use of these terms reflects the ranking of trainees in the final selection process.
methodsA descriptive study analyzing LORs submitted by Canadian medical graduate applicants to the University of Ottawa General Surgery Program in 2019 was completed. We collected demographic information about applicants and referees and recorded the use of preidentified quantitative descriptors (eg, best, above average). A 10% audit of the data was performed. Descriptive statistics were used to analyze the demographics of our letters as well as the frequency of use of the quantitative descriptors.
resultsThree hundred forty-three LORs for 114 applicants were analyzed. Eighty-five percent (291 of 343) of LORs used quantitative descriptors. Eighty-four percent (95 of 113) of applicants were described as above average, and 45% (51 of 113) were described as the "best" by at least 1 letter. The candidates described as the "best" ranked anywhere from second to 108th in our ranking system.
conclusionsMost LORs use quantitative descriptors. These terms are generally positive, and while the use does discriminate between different applicants, it was not helpful in the context of ranking applicants in our file review process.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.