Instagram Use Among Orthopaedic Surgery Residency Programs
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic created unprecedented challenges to residency recruitment. With in-person away rotations prohibited and interviews held virtually, orthopaedic residency programs turned to social media. Studies document the exponential growth of residency program Instagram accounts after March 2020, but few analyze the content of their posts. This study provides an updated assessment of such Instagram accounts including a detailed analysis of their content and a discussion of potentially concerning posts. METHODS: Orthopaedic surgery residency programs participating in the National Resident Matching Program and any Instagram accounts associated with these programs were identified. Instagram accounts were analyzed, and the 25 most recent posts and all highlighted stories for each account were coded for content based on a predetermined list of categories. Specific attention was given to content that may raise legal, ethical, or professionalism concerns. The primary outcome was the most common content code among posts. The secondary outcomes were the number of posts identified as potentially concerning and the types of concerns represented. RESULTS: Overall, 138 of 193 residency programs (72%) had an Instagram account at the time of cross-sectional analysis, 65% of which were created between April and December 2020. All accounts were public. Profiles had on average 1,156 ± 750 followers and 59 ± 75 posts. Of the 3,348 posts analyzed, the most common coded themes were resident introductions (33%), camaraderie (27%), and social life and hobbies (26%). There were 81 concerning posts from 52 separate accounts. Seventy-five of the concerning posts (93%) depicted residents scrubbed alone. CONCLUSION: Orthopaedic residency Instagram accounts are potential tools for residency recruitment and can depict a program's culture through posts over time. However, public accounts are open to scrutiny by other viewers, including patients and their families. Care must be taken to consider multiple perspectives of post content, so as to bolster, not damage, the residency program's reputation.
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 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".