Referrals for pediatric weight management: the importance of proximity
Bibliographic record
Abstract
BACKGROUND: Limited access to weight management care can have a negative impact on the health and well-being of obese children and youth. Our objectives were to describe the characteristics of clients referred to a pediatric weight management centre and explore potential differences according to proximity. METHODS: All demographic and anthropometric data were abstracted from standardized, one-page referral forms, which were received by a pediatric weight management centre in Edmonton, AB (Canada) between April, 2005 and April, 2009. RESULTS: Referrals (n = 555; 52% male; age [mean +/- standard deviation]: 12.4 +/- 2.6 y; BMI: 32.3 +/- 6.8 kg/m2; BMI percentile: 98.4 +/- 1.7; BMI z-score: 2.3 +/- 0.4) were received from 311 physicians. Approximately 95% of referrals were for boys and girls classified as obese or very obese. Based on postal code data, individuals were dichotomized as either living within (local; n = 455) or beyond (distant; n = 100) the Edmonton Census Metropolitan Area. Numerous families resided several hundred kilometres away from our centre. Overall, distant clients were taller, weighed more, and were more overweight than their local counterparts. For distant clients, the degree of overweight was higher in youth versus children. CONCLUSION: Pediatric weight management services must be designed to optimize access to health services, especially for distant clients who may be at increased obesity-related health risk.
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.
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 | 0.015 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".