Exploring Social Media’s Potential in Interprofessional Education
Bibliographic record
Abstract
Social media applications such as Facebook, Twitter, and blogs have become part of mainstream society and are currently being used throughout health professions education. The asynchronous nature and conversational aspects of social media permit learners to collaborate with and learn from others in different fields. Interprofessional education (IPE) is a growing paradigm in health professions schools for a variety of reasons, including the desire to teach future practitioners how to communicate with each other and engage in collaborative care. Due to the interdisciplinary nature of IPE curricula, those programs must overcome numerous logistical barriers to be successful. Finding suitable times and locations for interprofessional students to meet and arranging opportunities for them to collaborate on healthcare issues are just two of the logistical impediments to IPE implementation. Fortunately, the asynchronous, conversational, and collaborative aspects of social media applications enable them to facilitate interprofessional communication and alleviate some of the time and space issues. In this article, we describe in further detail the merits of social media relevant to IPE, provide specific examples of how social media can be used to enhance aspects of IPE programs, and make a call for further research in this area.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".