Thou shalt not tweet unprofessionally: an appreciative inquiry into the professional use of social media
Bibliographic record
Abstract
BACKGROUND: Social media may blur the line between socialisation and professional use. Traditional views on medical professionalism focus on limiting motives and behaviours to avoid situations that may compromise care. It is not surprising that social media are perceived as a threat to professionalism. OBJECTIVE: To develop evidence for the professional use of social media in medicine. METHODS: A qualitative framework was used based on an appreciative inquiry approach to gather perceptions and experiences of 31 participants at the 2014 Social Media Summit. RESULTS: The main benefits of social media were the widening of networks, access to expertise from peers and other health professionals, the provision of emotional support and the ability to combat feelings of isolation. CONCLUSIONS: Appreciative inquiry is a tool that can develop the positive practices of organisations and individuals. Our results provide evidence for the professional use of social media that may contribute to guidelines to help individuals realise benefits and avoid harms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".