Evaluating the Utility and Impact of Canadian Plastic Surgery Residency Programs’ Instagram Accounts on Resident Recruitment and Engagement
Bibliographic record
Abstract
Introduction: This study assesses how Canadian Plastic Surgery Residency Instagram accounts are utilized and perceived by residents, fellows, and attending physicians, and evaluates their influence on medical students’ residency program selection. Methods: This 2-part study includes: (1) a descriptive analysis of Instagram activity, content, and engagement, along with a national survey of Canadian plastic surgery residents, fellows, and attendings assessing account utility; and (2) a survey of medical students who attended the University of Toronto Plastic Surgery Residency Information Session, evaluating Instagram's influence on residency selection. Descriptive statistics were reported. Results: Twelve of 13 Canadian Plastic Surgery Residency Programs had active Instagram accounts. Canadian Plastic Surgery Residency Instagram accounts had an average of 119 posts (SD = 94) over 5 years (SD = 2). Among surveyed residents ( N = 27/77, 35%) and fellows/attendings ( N = 83/328, 25%), Instagram use was reported by 93% and 81%, respectively. Resident recruitment ranked as the top goal (residents 1.75, fellows/attendings 3.17), followed by achievement highlights. Most residents (80%) and fellows/attendings (53%) felt medical students benefitted most. Preferred content included program culture (85%, 84%), resident profiles (90%, 73%), and research highlights (70%, 70%). Among medical student respondents ( N = 25/112, 22%), 95% followed Canadian programs on Instagram, seeking program culture, resident profiles, and educational opportunities (all 89%). Over half (56%) said Instagram influenced their perception of a program, with all reporting a positive impact. Conclusion: Instagram is a valuable platform for Canadian Plastic Surgery Residency Programs to share insights and influence medical student decision-making.
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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 | 0.007 | 0.172 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".