Personalizing obesity assessment and care planning in primary care: patient experience and outcomes in everyday life and health
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
Obesity is a complex, chronic disease, frequently associated with multiple comorbidities. Its management is hampered by a lack of translation of evidence on chronicity and pathophysiology into clinical practice. Also, it is not well understood how to support effective provider-patient communication that adequately addresses patients' personal root causes and barriers and helps them feel capable to take action for their health. This study examined interpersonal processes during clinical consultations, their impacts, and outcomes with the aim to develop an approach to personalized obesity assessment and care planning. We used a qualitative, explorative design with 20 participants with obesity, sampling for maximum variation, to examine video-recorded consultations, patient interviews at three time points, provider interviews and patient journals. Analysis was grounded in a dialogic interactional perspective and found eight key processes that supported patients in making changes to improve health: compassion and listening; making sense of root causes and contextual factors in the patient's story; recognizing strengths; reframing misconceptions about obesity; focusing on whole-person health; action planning; fostering reflection and experimenting. Patient outcomes include activation, improved physical and psychological health. The proposed approach fosters emphatic care relationships and sensible care plans that support patients in making manageable changes to improve health.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it