Assessment and Evaluation of Social Engagement in Dermatology Residency Programs on Instagram: Cross-sectional Study
Bibliographic record
Abstract
Background Without traditional in-person experiences due to COVID-19, dermatology residency applicants and programs had to search for new ways to get to know one another. Thus, many programs created or enhanced their social media accounts, specifically Instagram, providing an avenue for applicants. The Instagram Engagement Score (IES) is a tool that quantifies an Instagram account’s engagement. Objective We assessed the factors that influence a dermatology residency program Instagram account's total followers count and IES. Methods Accreditation Council of Graduate Medical Education-accredited dermatology residency programs in the United States were identified and evaluated on 3/6/2021-3/7/2021. Posts were categorized into educational, departmental, academic and professional, social, or other posts. Results 78 residency programs have Instagram accounts. 69 accounts were active, or posting after November 2020. Other than posts, Instagram Stories was used most frequently (51%). 60 accounts opened in 2020. University of Miami had the most followers (N=2260) while University of Kansas had the highest IES (IES=23.76). Program location and affiliation did not affect total followers or IES. Utilizing Instagram TV (p=0.019) significantly increased total followers, but not IES. Using linear correlation, total posts and departmental posts correlated with increased total follower count (p<0.001, p=0.018 respectively) and IES (p<0.001, p=0.008 respectively). Conclusions Instagram is a valuable platform for a dermatology residency program’s self-promotion and recruitment following COVID-19. We recommend dermatology residency programs to open an Instagram account and make more posts, especially departmental content.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".