Personalizing obesity assessment and care planning in primary care: patient experience and outcomes in everyday life and health
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.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".