Understanding Academic Clinicians' Decision Making for the Treatment of Childhood Obesity
Bibliographic record
Abstract
BACKGROUND: Although most clinicians agree that obesity is a major problem, treatment rates remain low. We conducted this discrete choice experiment (DCE) to understand academic clinicians' decisions in treating childhood obesity. METHODS: A total of 198 academic pediatric surgeons, pediatricians, family physicians, and allied health professionals were recruited from 15 teaching hospitals across Canada to participate in this DCE. Participants completed 15 tasks choosing between three obesity treatment scenarios to identify the scenario in which they would most likely treat pediatric obesity. RESULTS: Latent class analysis revealed two classes with early intervention and late intervention preferences. Participants in the early intervention group (30%) were sensitive to variations in patient and family support. They would likely intervene if patients were obese, with normal lipid levels, were prediabetic, had high blood pressure, and when obesity was lifestyle associated. Late intervention clinicians (70%) were more likely to intervene if patients were morbidly obese, had abnormal lipid levels, required insulin for diabetes, had very high blood pressure, or when obesity impacted the patient's mental health. Simulations predicted that increasing colleague support for intervention, providing expert consultation, and mobilizing multidisciplinary support would increase the likelihood of treating pediatric obesity earlier from 16.1% to 81.5%. CONCLUSIONS: This DCE was implemented to understand the factors clinicians use in making decisions. Most academic clinicians choose to intervene late in the clinical course when more-severe obesity-related morbidities are present. Increased support from colleagues, expert consultation, and multidisciplinary support are likely to lead to earlier treatment of obesity among academic clinicians caring for children.
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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 | 0.026 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".