Health advice and education given to overweight patients by primary care doctors and nurses: A scoping literature review
Bibliographic record
Abstract
Health advice for overweight patients in primary care has been a focus of obesity guidelines. Primary care doctors and nurses are well placed to provide evidence based preventive health advice. This literature review addressed two research questions: 'When do primary care doctors and nurses provide health advice for weight management?' and 'What health advice is provided to overweight patients in primary care settings?' The study was conducted in the first half of 2018 and followed Arksey and O'Malley (2005) five stage framework to conduct a comprehensive scoping review. The following databases were searched: Emcare, Ovid, Embase, The Cochrane library, Proquest family health, Health source (nursing academic), Joanna Briggs Institute EBP database, Medline, PubMed, Rural and remote, Proquest (nursing and allied health) and TRIP using search term parameters. Two hundred and forty-eight (248) articles were located and screened by two reviewers. Twenty-three research papers met the criteria and data were analysed using a content analysis method. The results show that primary care doctors and nurses are more likely to give advice as BMI increases and often miss opportunities to discuss weight with overweight patients. Body Mass Index (BMI) is often wrongly categorised as overweight, when in fact it is in the range of obese, or not recorded and when health advice is given, it can be of poor quality. Few studies on this topic included people under 40 years, practice nurses as the focus and those with a BMI of 25-29.9 without a risk factor. A 'toolkit' approach to improve advice and adherence to evidence based guidelines should be explored in future research.
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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 | 0.015 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".