Obesity Management in Primary Health Care: Front-Line Providers’ Experiences and Views
Bibliographic record
Abstract
As in the rest of the world, obesity in Oman has increased and according to World Health Organization (WHO) data, prevalence of obesity in 2008 and 2016 were 20.9% and 27% respectively. This study explores primary care physicians’ current strategies and management of obesity, attitude and perceptions towards obesity, educational needs, and their views on long-term follow up. Methods: A cross sectional study was conducted where practicing family medicine physicians from different governorates were invited to participate in an online questionnaire-based survey. Participant were invited via email and responses were kept anonymous. Responses were collected over three weeks in April 2019 and only responses that met inclusion criteria were analyzed with SPSS v22. Results: 77 complete responses met inclusion criteria and female were the majority (67.5%). Half of participants had less than 10 years of experience. Weight and BMI were recorded routinely by two-thirds of participants whereas waist- hip ratio was recorded by only 12%. Weight reduction medications were prescribed by 5.2% and 24% would refer an obese patient to Bariatric center. Main barrier to obesity management and referral was inadequate obesity specialist centers followed by short consultation times. The pathophysiology mechanism of obesity and related hormones was only known by 40.8%. Almost all participants agreed that formal obesity management training should be integrated as part of residency training. Conclusion: Despite the significant number of comorbidities related to obesity and its complications, weight, BMI and other anthropometric measures were not routinely performed. Nationally, the rate of referral to bariatric centers for evaluation is low. Boundaries and challenges do exist and need to be addressed. Obesity and weight management need to be integrated as part of Family Physicians Training Program.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".