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. Unique strengths of this study include recruiting adolescents and caregivers who had been referred to but did not attend or dropped out of a WM program. Research often neglects to include the nuanced experiences of program non-completers in examining factors influencing program initiation and engagement. Thus, including the experiences of this underrepresented group is critical to aid in our understanding of existing disparities in WM programs. Interviews revealed that participants needed more information regarding the WM program and misconceptions about program involvement. Consistent with previous research, individuals from racial, ethnic, and social-disadvantaged backgrounds have been shown to have a lower level of health literacy (Rikard et al., 2016). Research highlights that lower health literacy can lead to poorer health outcomes, difficulty managing chronic diseases, and lower utilization of health services (Fernandez et al., 2016; Miller, 2016). Incorporating health literary practices into clinical research is imperative to foster patient understanding, the ability to make informed choices about health, and increase program engagement in underrepresented groups (Bader et al., 2022). Future research should tailor interventions to meet the needs of patients with varying degrees of health literacy by utilizing a multitude of approaches, including simplifying written materials, modifying the consent process, presenting information in multiple formats, and offering in-person question-and-answer sessions with the intervention team. Adopting patient-oriented approaches in health service research may be one way to increase health literacy and reduce health disparities (Lastrucci et al., 2019). Active patient involvement in developing and supporting research delivery and evaluation improves the quality of patient care and positively affects outcomes (Forsythe et al., 2019; Manafo et al., 2018). Future research is encouraged to increase collaboration with patients, caregivers, and stakeholders (e.g., clinicians, policymakers) in the planning, conducting, and disseminating of research through engaging in advisory panels and embedded advisers to ensure focus on patient-identified priorities. More research is needed to explore the impact of collaborating with patient partners and its potential to reduce health disparities. Moreover, future research should also acknowledge the intersection of race and socioeconomic disadvantage by building community trust and intentionally utilizing recruitment strategies to accommodate family needs and competing life demands. Although Darling et al. (this issue) quantified low-income individuals as those utilizing public insurance, collecting other factors affecting participants’ financial security could aid in our understanding of the nuanced experiences of underrepresented groups. Importantly, Darling and colleagues (this issue) identified caregivers as the drivers of program initiation and ongoing engagement. Results revealed that engaged participants were initially disinterested in the WM program, yet having an interested caregiver led to ongoing program engagement. Indeed, caregivers significantly influence adolescent health behaviors and weight status through key personal affective factors (e.g., self-efficacy), dietary, and physical activity behaviors (Lytle, 2009). As suggested by Darling et al. (this issue), increasing caregiver understanding of the importance of WM programs for adolescents may be crucial to improve adolescent engagement. Research supports the importance of continued caregiver involvement for adolescents as a key to program success, including joining behavioral change efforts (Hess et al., 2022; Jones et al., 2019). Parent’s readiness for change, perception of the child’s weight status, and recognition of weight as a problem have been identified as critical considerations in adolescents’ ability to make and maintain lifestyle changes (Jones et al., 2011; Park et al., 2014) and there is a dearth of research examining how to effectively engage caregivers in WM interventions. However, further qualitative studies are needed with adolescents, caregivers, and clinicians to identify ways to optimize WM-based intervention delivery for adolescents from low-income backgrounds while engaging caregivers. Identifying and understanding the role of caregiver involvement, perceptions, and recognition of weight as a problem alongside environmental factors (e.g., access to transportation to sessions and food security) may bolster the potency and sustainability of WM programs. Considerable work is required to address the persistent health disparities in WM programs. Darling et al. (this issue) contribute meaningful scholarship by taking an important step forward in ameliorating disparities by exploring salient factors that must be addressed to engage adolescents and their caregivers referred to WM programs from low-income backgrounds. Future research maximizing the potential implementation and benefits of patient-oriented methods and caregiver involvement in WM programs holds promise. These results lay a solid foundation for improved WM interventions acknowledging the intersection of race and socioeconomic disadvantage to support engagement in adolescent healthcare programs. None declared. No new data were generated or analysed in support of this research.
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 | 0.015 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.068 | 0.036 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".