Clinical review: modified 5 As: minimal intervention for obesity counseling in primary care.
Bibliographic record
Abstract
OBJECTIVE: To adapt the 5 As model in order to provide primary care practitioners with a framework for obesity counseling. SOURCES OF INFORMATION: A systematic literature search of MEDLINE using the search terms 5 A's (49 articles retrieved, all relevant) and 5 A's and primary care (8 articles retrieved, all redundant) was conducted. The National Institute of Health and the World Health Organization websites were also searched. MAIN MESSAGE: The 5 As (ask, assess, advise, agree, and assist), developed for smoking cessation, can be adapted for obesity counseling. Ask permission to discuss weight; be nonjudgmental and explore the patient's readiness for change. Assess body mass index, waist circumference, and obesity stage; explore drivers and complications of excess weight. Advise the patient about the health risks of obesity, the benefits of modest weight loss, the need for a long-term strategy, and treatment options. Agree on realistic weight-loss expectations, targets, behavioural changes, and specific details of the treatment plan. Assist in identifying and addressing barriers; provide resources, assist in finding and consulting with appropriate providers, and arrange regular follow-up. CONCLUSION: The 5 As comprise a manageable evidence-based behavioural intervention strategy that has the potential to improve the success of weight management within primary care.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".