Online Profile of Canadian Diagnostic Radiology Residents: Do Residents Alter Their Profile When Applying for Fellowships?
Bibliographic record
Abstract
<ns4:p>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.</ns4:p>
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".