Background: Although there is increasing pressure on Universities to implement e-learning, this 'glorious revolution' has been met with disappointing results and universities have struggled to engage academic staff, who are major stakeholders, with its use. Although literature suggests online teaching adds to traditional faculty workload, information surrounding the actual 'cost' to individuals is sparse. For academics involved in postgraduate clinical education, it is even more incomplete. Involvement can be a risky undertaking for academics unfamiliar with the resources required. Aims: This study outlines staff resources required to create an e-module for busy, practicing clinicians. Method: Data (web analytics, email traffic, and work logs) was collected and statistical analysis performed outlining time involved, work patterns and responsibilities. Results: Data analysis revealed 75% of academic time occurred out of normal office hours. Sixteen total staff hours (12 planning and four delivery) were required to support one hour student online activity. Technical responsibilities were essential throughout, but unpredictable. Conclusions: Universities struggle to engage staff with e-learning due to its unrecognized and (many academics believe) unsustainable workload. Avoiding 'traditional' workload assumptions that are inaccurate, this study provides academics and managers involved in clinical education clear guidance and an increased understanding of workload with a goal to inform practice.

Universities struggle to engage staff with DL due to its unrecognised and (many academics believe) unsustainable workload. These 12 tips provide academics and managers involved in clinical education with clear guidance surrounding strategies that inform practice. New roles have emerged, work habits must be revolutionised and changes in routine must be addressed.

Background The rise of social media [SoMe] has changed medical education practice, possibly facilitating learning through conversational interaction, social feedback and relationships. Usage of newer SoMe tools like Instagram and Snapchat has not been scrutinised. This study aimed to understand how medical students may use newer SoMe tools, specifically Twitter, Instagram & Snapchat, in their learning, in the context of a parallel SoMe course. Methods An optional, parallel SoMe course was established at Newcastle University Medical School. 301 fourth-year medical students were invited to engage using Twitter, Snapchat and Instagram. Evaluation adopted a mixed methods approach, gathering SoMe analytics and survey data as well as qualitative, free-text responses from a questionnaire and focus-group discussion. Results Live-tweeting lectures featured 95 facilitator tweets, with five replies by students. 22 Instagram posts received no student responses, and three Snapchat stories were viewed 15,312 times, with 212 screenshots taken. Of questionnaire respondents, 75% [n=66] stated they engaged with content. Framework analysis of free-text responses and focus group discussion identified peer influence, fear of exposure, cognitive load and curiosity as drivers in new SoMe use.

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