‘Uncrunching’ time: medical schools’ use of social media for faculty development
Bibliographic record
Abstract
PURPOSE: The difficulty of attracting attendance for in-person events is a problem common to all faculty development efforts. Social media holds the potential to disseminate information asynchronously while building a community through quick, easy-to-use formats. The authors sought to document creative uses of social media for faculty development in academic medical centers. METHOD: In December 2011, the first author (P.S.C.) examined the websites of all 154 accredited medical schools in the United States and Canada for pages relevant to faculty development. The most popular social media sites and searched for accounts maintained by faculty developers in academic medicine were also visited. Several months later, in February 2012, a second investigator (C.W.S.) validated these data via an independent review. RESULTS: Twenty-two (22) medical schools (14.3%) employed at least one social media technology in support of faculty development. In total, 40 instances of social media tools were identified--the most popular platforms being Facebook (nine institutions), Twitter (eight institutions), and blogs (eight institutions). Four medical schools, in particular, have developed integrated strategies to engage faculty in online communities. CONCLUSIONS: Although relatively few medical schools have embraced social media to promote faculty development, the present range of such uses demonstrates the flexibility and affordability of the tools. The most popular tools incorporate well into faculty members' existing use of technology and require minimal additional effort. Additional research into the benefits of engaging faculty through social media may help overcome hesitation to invest in new technologies.
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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 | 0.018 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".