Knowledge and Training Needs of Primary Healthcare Physicians Regarding Obesity Management in Saudi Arabia: A Systematic Review
Bibliographic record
Abstract
Background/Objectives: Obesity has surged globally, posing various health risks. Its prevalence and management, especially in primary health care settings in Saudi Arabia, have recently been under the spotlight. This research synthesized various studies, analyzing the knowledge and preparedness of primary health care physicians in addressing and management of obesity in Saudi Arabia. Methods: An exhaustive evaluation of studies spanning different regions of Saudi Arabia, focusing on physicians' capabilities, knowledge, and practices in obesity management. This analysis also took into account the comparative approaches of countries like Canada, the UK, USA, and Hungary. Various factors, such as specialized obesity treatments, patient-centric approaches in pediatric obesity, career stages of physicians, obesity's correlation with noncommunicable diseases, and nutritional competence, were studied. Results: Nine studies were included. From a collective pool of 2430 participants across the discussed studies, challenges in obesity management were consistent. Despite having an understanding of obesity, there was a significant knowledge gap in specialized treatments, with many physicians feeling unprepared to manage the condition. Factors such as geographical diversity, physicians' experiences, external influences on pediatric obesity management, and the correlation between obesity and other health conditions were highlighted. The need for effective nutrition care, despite perceived capability, was a notable finding. Conclusion: Saudi Arabia faces a significant challenge in obesity management in primary health care settings, marked by knowledge gaps and the need for continuous training. The analyzed studies emphasize the importance of tailored interventions, robust training modules, and public health campaigns within Saudi Arabia's unique context to combat the obesity epidemic effectively.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".