Motivational Interviewing (MI) in Obesity Care: Cultivating Person‐Centered and Supportive Clinical Conversations to Reduce Stigma: A Narrative Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Patients perceive high levels of weight prejudice, stigma, and discrimination within health systems, affecting their ability to manage their obesity and related chronic conditions. Scientific and patient obesity associations worldwide have prioritized the reduction of weight stigma to improve patient experiences in health systems and overall health outcomes. Since a significant proportion of the population is now living with multiple chronic diseases related to obesity, healthcare systems must shift toward multi-disease management frameworks incorporating person-centered and non-stigmatizing clinical conversations. Motivational Interviewing (MI) has the potential to transform clinical interactions by using non-stigmatizing language, communication, and practices. Studies using MI in obesity management have solely focused on weight loss outcomes, while other patient experience related outcomes would also be relevant to evaluate. Methods: A narrative review was undertaken to critically analyze the potential impact of MI on obesity and chronic disease management practices and experiences. Findings: An analysis and contextualization of the MI theoretical framework for obesity management, based on the philosophy of motivational spirit, was reviewed, assessing micro skills or strategies. Conclusion: MI may assist healthcare professionals conduct non-stigmatizing clinical conversations in accordance with basic principles of collaborative therapeutic alliances. A proposal for research considerations that can help illuminate the potential for of MI in obesity management is also outlined.
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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 | 0.019 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it