Current Impacts of The Health at Every Size Model Upon Adolescent Health Outcomes: A Literature Review
Bibliographic record
Abstract
Introduction: Child and adolescent obesity is a global concern. The number of youths with obesity is increasing exponentially with subsequent increases in obesity related co-morbidities. Current approaches to treat obesity include weight-normative style interventions which provide no longterm benefits and have increased risk of physical and psychological harms. The Health at Every Size (HAES) model is a safe and inclusive alternative to traditional diet and exercise prescriptions. This approach has been shown to improve patients’ overall health within adult populations. Based on these prior studies, HAES shows great promise for use in adolescent health. Objective: The purpose of this review is to consolidate the current literature available pertaining to use of Health at Every Size and other weight-inclusive strategies within the adolescent population. Through analysis of current evidence, this literature review aims to determine which of the five principles of the Health at Every Size model are effective for improving overall health in the adolescent population. Methods: A literature search using PubMed, Wiley Online Library and the University of Manitoba Online Library databases was conducted using key terms pertaining to Health at Every Size ideology and its associated principles. Eleven articles and one clinical practice guideline were found to meet the inclusion criteria and were included in this literature review. Results: Common themes found across the twelve documents reviewed included positive outcomes in both mental health and sustainable personal care practices in the weight-inclusive intervention groups. Both weight-inclusive and weight-normative groups found improvements in physical health measures. There were minimal improvements in participant weight biases and stigmatization in intervention groups. Conclusion: The Health at Every Size approach to adolescent obesity and health enhancement is effective for improving physical and psychological health. In addition, it teaches sustainable health practices with very little risk to the patient. Adolescence is a time of great change and vulnerability and using an approach that is protective of long-term physical and psychological health outcomes is of the utmost importance. Further research is required on promotion and incorporation of this strategy into primary adolescent care.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| 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".