<p>Translating knowledge into action to prevent pediatric and adolescent diabesity: a meeting report</p>
Bibliographic record
Abstract
BACKGROUND: The obesity and Type 2 Diabetes Mellitus (T2DM) rates are at an all-time high globally. This diabesity epidemic is increasingly impacting children and adolescents, and there is scarce evidence of interventions with favourable long-term outcomes. PURPOSE: In order to understand the determinants of diabesity and how to address them, multiple stakeholders were invited to a meeting to discuss current state of knowledge and to help design a program to prevent pediatric and adolescent diabesity. PARTICIPANTS AND METHODS: The meeting was held at McMaster University on March 4th, 2015. The event involved presentations to deliver state-of-the-art knowledge about diabesity, and roundtable discussions of several domains including nutrition, physical activity, sleep, and mental health. Discussion transcripts were analyzed using NVivo. RESULTS: Forty-nine participants took part in the workshop. They included clinical healthcare professionals, public health, Aboriginal Patient Navigator, research scientists, students, and patients with family members. A total of 628 reference counts from the roundtable discussions were coded under 20 emerging themes. Participants believed that the most important elements of the program involve the provision of knowledge and education, family involvement, patient motivation, location of program delivery, and use of surveys and questionnaires for outcome measurement. CONCLUSION: Effective pediatric and adolescent diabesity prevention programs should be conceptualized by multidisciplinary stakeholders and embrace the complexity of diabesity with multiprong interventions. This meeting provided a framework for developing such interventions.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".