Primary care and primary healthcare in obesity management
Bibliographic record
Abstract
RECOMMENDATIONS 1. We recommend that PHPs identify PLWO, and initiate patient-centred, health-focused conversations with them (Level 3, Grade C). 2. We recommend that PHPs ensure that they ask PLWO for their permission prior to discussing weight or taking anthropometric measurements (Level 3, Grade C). 3. Primary care interventions should be used to increase health literacy in individuals' knowledge about and skills in weight management as an effective intervention to manage weight (Level 1a, Grade A). 4. PHPs should refer PLWO to primary care multi-component programmes with personalised obesity management strategies as an effective way to support obesity management (Level 1b, Grade B). 5. PHPs can use collaborative deliberation with motivational interviewing to tailor action plans to individuals' life context in a way that is manageable and sustainable to support improved physical and emotional health, and weight management (Level 2b, Grade C). Features of primary care and primary healthcare community-based interventions for PHPs and developers: 6. Interventions that target a specific ethnic group should consider the diversity of psychological and social practices with regard to excess weight, food and physical activity as well as socioeconomic circumstances, as they may differ across and within different ethnic groups (Level 1b, Grade B). 7. Longitudinal primary care interventions should focus on incremental, personalised, small behaviour changes (the 'Small Changes' approach) to be effective in supporting people to manage their weight (Level 1b, Grade B). 8. Primary care multi-component programmes should consider personalised obesity management strategies as an effective way to support PLWO (Level 1b, Grade B). 9. Primary care interventions that are behaviour based (nutrition, exercise, lifestyle), alone or in combination with pharmacotherapy, should be utilised to manage PLWO (Level 1a, Grade A). 10. Group-based nutrition and physical activity sessions informed by the Diabetes Prevention Program and the Look AHEAD (Action for Health in Diabetes) programme should be used as an effective management option for PLWO (Level 1b, Grade A). 11. Interventions that use technology to increase reach to larger numbers of people asynchronously should be a potentially viable lower-cost method in a community-based setting (Level 1b, Grade B). Educational recommendations to support development of obesity management skills in the primary healthcare clinical workforce: 12. Educators in undergraduate, graduate and continuing education programmes for PHPs should provide courses and clinical experiences to address the gaps in skills, knowledge of the evidence, and attitudes necessary to confidently and effectively support PLWO (Level 1a, Grade A).[20].
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".