Commentary: Addressing Health Disparities in Hospital-Based Interventions for Adolescents and Their Caregivers
Bibliographic record
Abstract
Adolescent obesity is a complex and significant public health concern affecting over 340 million children and adolescents aged 5–19 worldwide in 2016 (WHO, 2021). To mitigate such risks, the U.S. Preventative Services Task Force recommended behavioral weight management (WM) interventions for children >6 years of age (Grossman et al., 2017). However, the prevalence of income disparities in structured WM programs amongst adolescents from lower-income backgrounds exhibits fewer positive outcomes (e.g., reduction in weight, body fat, blood pressure) than peers from middle-class backgrounds (Demeule-Hayes et al., 2016; Kalarchian et al., 2009). Concerningly, adolescents from low-income backgrounds have higher attrition and less engagement in structured WM programs suggesting a need to identify the specific mechanisms influencing the lack of engagement with this population (Hawkins et al., 2018; Rhodes et al., 2017). Kilbourne and colleagues’ (2006) framework highlights the necessity to identify existing health disparities (phase 1), specific mechanisms driving health disparities (phase 2), and action on interventions and policy change (phase 3) to understand, reduce, and eliminate gaps in healthcare. (Darling et al., this issue)’ qualitative study (this issue) goes beyond describing existing differences in access to care, expanding our understanding of specific factors influencing WM program initiation, engagement, and attrition amongst adolescents and their caregivers from low-income backgrounds. Overall, findings highlight a clear gap concerning participants’ comprehension of program goals, misconceptions, and the importance of engaging caregivers to enhance adolescent program engagement and positive outcomes. There is no doubt that this study fills a significant gap in the literature; however, it is important to consider (a) strengths and (b) future directions to inform research in the broader context of pediatric psychology.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".