In response to The role of smartphones in encouraging physical activity in adults
Bibliographic record
Abstract
In response to The role of smartphones in encouraging physical activity in adults Aaina Mittal,1 Shyam Gokani,1 Alexander Zargaran,2 Javier Ash,1 Georgina Kerry,3 Dara Rasasingam1 1Department of Medicine, Imperial College School of Medicine, Imperial College London, London, 2Department of Medicine, St. George’s, University of London, London, 3Department of Medicine, University of Birmingham Medical School, Birmingham, UK We read with great interest the article by Stuckey et al1 entitled “The role of smartphones in encouraging physical activity in adults” recently published in the International Journal of General Medicine. As the article identifies, “lack of physical activity is a global public health issue”,1 so finding ways of encouraging it is essential to better health outcomes worldwide. Bearing this in mind and recognising the article has set groundwork for prospective exploration in the areas it addresses, scope for future research in this area can be identified. Authors' replyMelanie I Stuckey,1 Shawn W Carter,2 Emily Knight3 1Research and Academics, Ontario Shores Centre for Mental Health Sciences, Whitby, ON, Canada, 2Eating Disorder Residential Program, Ontario Shores Centre for Mental Health Sciences, Whitby, ON, Canada, 3Faculty of Health Sciences, University of Western Ontario, London, ON, Canada Thank you for providing the opportunity to respond to the letter written by Mittal et al in response to our paper titled “The role of smartphones in encouraging physical activity in adults.”1 We generally agree with their comments, but add considerations for each of their three suggestions. View the original paper by Stuckey and colleagues.
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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 | 0.006 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.020 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".