Supplemental material - A Randomized Trial of Behavioral Nudges Delivered Through Text Messages to Increase Influenza Vaccination Among Patients With an Upcoming Primary Care Visit
Bibliographic record
Abstract
Supplemental material for A Randomized Trial of Behavioral Nudges Delivered Through Text Messages to Increase Influenza Vaccination Among Patients With an Upcoming Primary Care Visit by Mitesh Patel, Katherine L. Milkman, Linnea Gandhi, Heather N. Graci, Dena Gromet, Hung Ho, Joseph S. Kay, Timothy W. Lee, Jake Rothschild, Modupe Akinola, John Beshears, Jonathan E. Bogard, Alison Buttenheim, Christopher Chabris, Gretchen B. Chapman, James J. Choi, Hengchen Dai, Craig R. Fox, Amir Goren, Matthew D. Hilchey, Jillian Hmurovic, Leslie K. John, Dean Karlan, Melanie Kim, David Laibson, Cait Lamberton, Brigitte C. Madrian, Michelle N. Meyer, Maria Modanu, Jimin Nam, Todd Rogers, Renante Rondina, Silvia Saccardo, Maheen Shermohammed, Dilip Soman, Jehan Sparks, Caleb Warren, Megan Weber, Ron Berman, Chalanda N. Evans, Seung Hyeong Lee, Christopher K. Snider, Eli Tsukayama, Christophe Van den Bulte, Kevin G. Volpp, and Angela L. Duckworth in American Journal of Health Promotion
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.457 | 0.017 |
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".