A Patient-centred Approach to Obesity: Counselling Health Behaviour Change
Bibliographic record
Abstract
Family physicians (FPs) commonly deal with patients’ concerns about weight, because of a direct request for advice on losing weight or because a medical assessment leads to concerns about a patient’s overall health risks. As with other chronic conditions such as diabetes or hypertension, some people are predisposed to obesity because of a genetic tendency; the incidence is also influenced by environmental factors. Attempts at weight loss are frustrating for both patients and physicians, because patients have difficulty sustaining long-term weight reduction. An estimated two-thirds of the weight loss achieved by individual patients is regained in the year after the initial loss.1 A patient-centred approach to obesity takes into account such factors as stage of change, level of motivation, health beliefs, support system, family background and other family factors, and psychosocial stress. This approach may improve overall patient care.2,3 Factors beyond diet, exercise, and medications must be considered. FPs need to find ways to avoid frustration and engender optimism in their patients. In addition, we must recognize the role families play in contributing to and perpetuating obesity in patients, especially children.
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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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.006 |
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".