Awareness and Level of Knowledge About Surgical Site Infections and Risks of Wound Infection Among Medical Physicians in King Abdulaziz University Hospital: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Surgical site infections (SSIs) are one of the leading causes of death, and its prevention is a key element of applying the concept of patient safety and quality care. OBJECTIVE: This study aimed to assess the level of knowledge about SSIs and risks of wound infection among medical physicians in King Abdulaziz University Hospital. METHODS: All surgical and medical consultants, specialists, residents, and medical interns were invited to participate in the study. A 20-Item multiple-choice questionnaire was developed by reviewing the previous literature and with the help of a group of certified surgeons to assess the level of knowledge in all participants. RESULTS: A total of 119 doctors were included in this study. Among all respondents, 92 (77.3%) were intern doctors, 16 (13.4%) were resident doctors, and 11 (9.2%) were specialist doctors. Moreover, 66 (55.5%) doctors knew the definition of SSI. Only one-quarter, that is, 30 (25.2%) doctors knew about the incidence of SSI. In addition, 8 doctors (6.7%) had good knowledge, 75 (63.0%) had fair knowledge, and 36 (30.2%) had poor knowledge regarding SSI according to this study. CONCLUSIONS: Level of knowledge about SSIs and risks of wound infections among medical physicians should be improved to ensure better wound care and quality care for the patients.
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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 | 0.001 | 0.003 |
| 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) | 0.002 | 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".