Building a Global, Online Community of Practice: The OPENPediatrics World Shared Practices Video Series
Bibliographic record
Abstract
PROBLEM: Health care professionals are familiar with engaging in local communities of practice (CoPs) within their hospital, region, and/or country, but despite the availability of online technologies that facilitate online global collaboration, the health care sector has yet to fully embrace these tools. APPROACH: In 2013, OPENPediatrics (an online social learning platform) launched the World Shared Practices video (WSP) series to engage and coalesce the global community of critical care clinicians. Each month, a 30- to 45-minute video featuring a pediatric critical care medicine expert, interspersed with questions for the audience, is released. Viewers contribute to the community discussion by leaving comments that display alongside the video. Clinicians are encouraged to asynchronously host an educational conference so they can watch the videos and participate in the discussion together. OUTCOMES: From March 2013-November 2015, 28 WSPs were launched on a variety of topics. They were viewed over 18,414 times by 1,864 viewers in 132 countries and 760 hospitals; 1,155 comments were submitted. Attending physicians/consultants were the largest audience (36% [671/1,864]), and 37% (30/81) of responding viewers that commented in WSPs watched in small groups. The WSP series was reported to add value to respondents' learning or teaching and to have had a positive impact on their knowledge or practice. NEXT STEPS: Future research will focus on further describing the context and structure of the CoP and on more deeply investigating its higher-level outcomes and impact. More work is needed to identify barriers and strategies that improve online community engagement.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".