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Record W2803740287 · doi:10.15694/mep.2018.0000102.1

Online Profile of Canadian Diagnostic Radiology Residents: Do Residents Alter Their Profile When Applying for Fellowships?

2018· article· en· W2803740287 on OpenAlexaffabout
Anthony Vo, Arlene Kanigan

Bibliographic record

VenueMedEdPublish · 2018
Typearticle
Languageen
FieldMedicine
TopicRadiology practices and education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDescriptive statisticsMedicineAudience measurementFamily medicineSelection (genetic algorithm)DemographyPsychologyStatisticsAdvertisingSociology

Abstract

fetched live from OpenAlex

This article was migrated. The article was marked as recommended. Objective: Survey the online profile of Canadian Diagnostic Radiology residents at the University of Alberta and determine whether residents alter it when applying for fellowships, due to the perceived assessment of their profile by Fellowship Selection Committees. Methods: A cross-sectional study was performed by distributing an anonymous questionnaire to 31 residents at the University of Alberta. Descriptive and ANOVA statistical analyses were performed. P-value less than 0.01 was considered statistically significant. Results: 26 questionnaires were completed. The average age was 28.9. 91.4% of residents have Facebook, followed by Instagram (30.4%) and ResearchGate (30.4%). 52.5% viewed their profile at least once daily, although 83.3% make changes to it less than once per month. The profiles were primarily for personal use (72.7%) and none were solely for professional use. 53.8% felt that Fellowship Selection Committees assess their profile and 69.2% were neutral or agreed with this. In anticipation, 70.6% would restrict profile viewership, while 29.4% would change their profile name, predominantly due to the sensitive and personal information. 92.8% would make the changes at least 2 months prior to the application deadline. There was no statistical difference between age and having a profile (p=0.597), agreement with Fellowship Selection Committees using a resident's profile for selection (p=0.91), how often residents viewed (p=0.827) or changed (p=0.934) their profile. Conclusion: Nearly all Canadian residents at our institution have an online profile and over half view it at least once daily. The majority of residents perceived that their profile is assessed by Fellowship Selection Committees, but are not against it. In anticipation, most residents would alter their profile prior to the application deadline mainly due to the sensitive and personal information.

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)0.001
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.730

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.049
GPT teacher head0.321
Teacher spread0.272 · 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 designObservational
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
Published2018
Admission routes2
Has abstractyes

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