Developing tools to support patients and healthcare providers when in conversation about obesity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract People living with obesity suffer from multiple health issues, including diabetes and mental health problems. Misinformation about the complex nature of this condition greatly affects the way one manages obesity. This results in unrealistic expectations by both healthcare providers and patients. Effective obesity management must be individually tailored for each patient. The objective of this project was to improve four communication tools by co-designing them with patients. A co-design approach was used to improve the efficacy and applicability of the tools through a working collaboration between patients, care providers, and researchers. While most articles describe processes to create shared-decision making (SDM) tools which compare alternative diagnosis and treatment options, few papers describe models to create SDM tools which go beyond showing benefits and risks. In this paper, we describe our process and approach to the re-design of four of the 5As obesity tools. We hope this study provides a valuable model for other teams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it