TYPE 2 DIABETES MELLITUS MANAGEMENT KNOWLEDGE AMONG PHC PHYSICIANS IN BURAIDAH CITY, 2020
Bibliographic record
Abstract
Introduction: Type 2 diabetes mellitus (T2DM) is highly prevalent in Saudi Arabia. primary health care (PHC) doctors provide most of T2DM medical care Objectives: To estimate PHC physicians and family medicine residents level of T2DM management knowledge as per Saudi national reference of clinical guidelines for care of diabetic patients. Methods: We conducted a cross-sectional study using a structured questionnaire. Beside general participant characteristics, we prepared 17 questions on four aspects of T2DM management. These were diagnosis, non-pharmacological and oral hypoglycemic agent, insulin and follow up. Each aspect was given a score of 4-6 points and the total score was 20 points. Result: Out of 258 physicians, 178 were actually available at the time of the survey and 106 completed the study questionnaire. The overall response rate was 41.1%. The mean age of participants was 34.1 years and around two thirds of them were males. In-training family medicine residents formed the largest segment, 45 (42.5%). Mean duration of practice was 7.8 years. The reported daily workload showed that more than one-third of physicians (36.5%) manage ≥ 20 patients per day and almost all of them manage T2DM. Out of the total 20 points, only one quarter of participants had scored more than 15 points, while another quarter could not achieve more than 40%. Only physician qualification had impacted physician performance. Conclusion: PHC doctors knowledge about T2DM management is sub-optimal. Properly selected educational activities targeted diabetes management are needed.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".