2362. Adoption and Utilization of Social Media among Adult and Pediatric Infectious Diseases Divisions and Fellowship Programs in the United States and Canada
Bibliographic record
Abstract
Abstract Background Social media (SoMe) is used by 72% of US adults. The utility of SoMe for infectious diseases (ID) has been highlighted by the COVID-19 pandemic. Adoption and utilization of SoMe by ID divisions is not well characterized. Methods We conducted a systematic search strategy for US and Canadian ID fellowship/division SoMe accounts – specifically Twitter, Facebook (FB), and Instagram (IG) – in November-December 2021. We collected data from program websites, SoMe accounts, and NRMP and FREIDA databases. Primary outcome was ID SoMe account prevalence. Secondary outcomes included measures of SoMe characteristics, adoption, and activity comparing adult and pediatric programs. We reported descriptive statistics. Results Of 243 ID programs in the US and Canada, 170 (70%) are adult and 73 (30%) are pediatric with 222 (91.4%) programs in the US. Twitter, FB, and IG accounts were found for 70 (31.5%), 14 (6.3%) and 14 (6.3%) US programs, respectively (Table 1); 2/21 (9.5%) Canadian programs had a SoMe account (Twitter). Twitter accounts were associated with larger programs and higher match rates (Table 2). SoMe activity and engagement before and during IDWeek 2021 is in Table 3. More adult programs had Twitter accounts (37.3% vs 17.2%, p=0.004), but utilization was similar between adult and pediatric programs. Most Twitter posts were educational (1653/2859, 57.8%); FB primarily promotional (68/128, 53.1%); IG mostly social (34/79, 43%). COVID-19 related posts were found in 821/2859 (28.7%) Twitter posts, 33/128 (25.8%) FB posts, and 5/79 (6.3%) Instagram posts. FB was the earliest adopted SoMe platform for ID, but Twitter and IG have more recent growth. The rate of Twitter account creation increased from 0.35 accounts/month prior to March 2020 (COVID pandemic declaration) to 1.95 accounts/month after March 2020. 71.4% (10/14) of IG accounts were created after March 2020. Conclusion SoMe remains underutilized across all ID divisions, but COVID-19 and virtual recruiting may have influenced recent account creation (Figure 1). Twitter is the most frequently used ID program SoMe platform in comparison to FB and IG, and use increased during IDWeek. ID divisions should embrace the digital and SoMe space with benefits for recruitment and amplification of their trainees, faculty, and specialty. Disclosures Jasmine R. Marcelin, MD, Pfizer/Mayo Clinic Global Bridges: Honoraria.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".