Physician-related predictors of referral for multidisciplinary paediatric obesity management: a population-based study
Bibliographic record
Abstract
BACKGROUND: It is recommended that primary care-based physicians refer children with overweight and obesity to multidisciplinary paediatric obesity management, which can help to improve weight and health. OBJECTIVE: To determine predictors of referral to multidisciplinary paediatric obesity management. METHODS: This retrospective, population-level study included physicians who could refer 2-17 years old with a body mass index ≥85th percentile to one of three multidisciplinary paediatric obesity management clinics in Alberta, Canada. Physician demographic and procedural data were obtained from Practitioner Claims and Provider Registry maintained by Alberta Health from January 2014 to December 2017. Physician characteristics were compared based on whether they did or did not refer children for obesity management. Univariable and multivariable logistic regression models analysed associations between physician characteristics and referral making. RESULTS: Of the 3863 physicians (3468 family physicians, 395 paediatricians; 56% male; 49.3 ± 12.2 years old; 22.3 ± 12.6 years since graduation) practicing during the study period, 1358 (35.2%) referred at least one child for multidisciplinary paediatric obesity management. Multivariable regression revealed that female physicians (versus males) [odds ratio (OR): 1.68, 95% confidence interval (CI): 1.46-1.93; P < 0.0001], paediatricians (versus family physicians) (OR: 4.89, 95% CI: 3.85-6.21; P < 0.0001) and urban-based physicians (versus non-urban-based physicians) (OR: 2.17, 95% CI: 1.79-2.65; P < 0.0001) were more likely to refer children for multidisciplinary paediatric obesity management. CONCLUSIONS: Approximately one-third of family physicians and paediatricians referred children for multidisciplinary paediatric obesity management. Strategies are needed to improve referral practices for managing paediatric obesity, especially among male physicians, family physicians and non-urban-based physicians as they were less likely to refer 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".