Facing obesity: Adapting the collaborative deliberation model to deal with a complex long-term problem
Bibliographic record
Abstract
OBJECTIVE: Care communication about obesity needs to respond to the complex biopsychosocial processes that affect weight and health. The collaborative deliberation model conceptualizes interpersonal work that underpins empathic communication and shared decision-making. The goal of this study was to elucidate how primary care practitioners can use the model to achieve shared obesity assessment and care planning. METHODS: This qualitative study used direct observation of clinical encounters with twenty patients with obesity sampled for maximum variation in context, semi-structured patient and provider interviews, patient journals and two follow-up interviews over eight weeks. Themes were compared to the original model. RESULTS: We identified five processes that may be relevant for collaborative deliberation about obesity in addition to the original model: (1) Exploring the story, (2) Reframing the story, (3) Co-constructing a new story, (4) Choosing a priority, and (5) Experimenting with alternatives. CONCLUSIONS: We propose an enhanced collaborative deliberation model for obesity that describes the interpersonal work needed before and after deliberation about preferences and courses of action. PRACTICE IMPLICATIONS: The enhanced model can support clinicians in achieving meaningful conversations about obesity and complex chronic disease resulting in care plans that are responsive to and achievable in the patient's lifeworld.
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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 | 0.025 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".