‘23 and ½ h’ goes viral: top 10 learnings about making a health message that people give to one another
Bibliographic record
Abstract
In my day job as a Family Physician, I often wonder, ‘Is this bacterial or viral?’ In my other job, where I try to innovate on how to engage patients in more meaningful ways, my question is slightly different: ‘How can we make this viral instead of bacterial?’ ‘23 ½ hours: what is the single most important thing you can do for your health?’1 (referred to as ‘23.5’ below and figure 1) is a video I posted on YouTube in December 2011. My objective in making the video was twofold: 1) to experiment in creating a new way of engaging patients about their health and 2) to answer what is the most important thing we can do for our health? I am a family doctor, not a sports medicine expert, so I was intrigued that my answer is exercise. I was intrigued as activity is something I ask my patients about but it is not something I have systematically assessed and counselled upon in my practice in the same way as other clinical problems such as blood pressure or cholesterol. Like any good virus, my primary objective was spread. At the time of writing (22 February 2012) 23.5 has had 2 million people sit down and view it, has averaged about 25 000 views a day, generated over 1000 comments and has been ‘liked’ by over 16 000 people (and ‘disliked’ by 190). It has already been translated by the ‘community’ into Spanish and …
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.117 | 0.040 |
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".