Social Networking in Medicine: The VIIth Nerve Facebook Page
Bibliographic record
Abstract
Background: Social networks including Facebook are gaining popularity in industry, however, health care has yetto fully embrace this trend. Social networking enables unprecedented speed and scope of communication andinformation sharing; mobile devices allow health professionals to feel connected to training hospitals, peers andmentors. The VIIth Nerve is a Facebook page for medical students worldwide, providing a platform to share medicalcases, educational videos and audio sounds. Methods: A Facebook page was developed and is ongoing. A research team has been established to design and runthe page, and collect data. Demographics, location of users, visits to the page, type of posts and total number ofusers were tracked. Results: To date, 161 users subscribed to the Facebook page and a total of 44,042 people have been reached. Sixtyfourper cent of subscribers are female, 89 per cent of whom fall between the ages of 18 and 34. Users are from ninecountries and eight different first languages are indicated. It has been found that users are most engaged by visualmaterials, namely videos clips and photos. Conclusion: Synergies between social networks and medicine is vastly underrepresented in the literature to date.Implications of social media in the learning environment may be of significant value and affect the future practiceof medicine. Future steps will include focus groups to recognize the benefits and pitfalls of using Facebook in thehealth care setting. This research effectively connects students internationally, and will determine how social mediacan effectively be implemented to prepare students for life-long learning.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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".