Exploring evidence-based interventions for obesity management
Bibliographic record
Abstract
Obesity is a major public health issue and imposes a significant burden on healthcare systems and people themselves. However, current obesity management practices often remain suboptimal, primarily due to the limited implementation of existing, evidence-based interventions. To bridge existing knowledge gaps, I implemented a series of studies in this PhD project to integrate insights from a culturally adapted, patient-centred, and obesity-specific intervention and broader chronic condition management approaches that can potentially inform obesity care. In the first study (Chapter 2), I explore the cultrually adapted Chinese version of the 5AsT model—a patient-centred obesity care model originally developed in Canada—within the Chinese healthcare context. To assess its potential for implementation, I conducted semi-structured interviews with healthcare professionals in China and Canada, assessing its cultural sensitivity and perceived fidelity. Additionally, I examined the perspectives of Chinese healthcare providers to identify key challenges and facilitators affecting its feasibility for implementation in a secondary healthcare setting in China. Given the coexistence of obesity and multiple long-term conditions and the shared challenges associated with managing these conditions, the subsequent two chapters (Chapters 3 and 4) explore evidence-based interventions potentially used for obesity management. The second study (Chapter 3) is a scoping review identifying care models currently implemented or potentially adaptable for managing multiple long-term conditions. While identifying such care models is crucial, it is equally important for stakeholders to understand and compare the outcomes of these models, including outcomes relevant to patients, healthcare providers, and other stakeholders. The third study (Chapter 4) is a systematic review explicitly focused on exploring and comparing outcomes reported from different care models identified in the second study across diverse healthcare settings. Throughout the studies in this thesis, I explore evidence-based interventions for obesity management, with a particular focus on patient-centred approaches. Additionally, I identify key opportunities and barriers to implementing these evidence-based interventions. Future work is needed to implement existing care models in the real world and evaluate them using qualitative or mixed-method studies, with a focus on generating critical insights into the barriers and facilitators that influence successful adoption.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".