Patients awareness of their medical conditions in multi-specialty outpatient clinics in Saudi Arabia
Bibliographic record
Abstract
OBJECTIVE: To assess the patients awareness of their medical conditions, identify the factors affecting their awareness, and assess patient's satisfaction with their doctors explanations of medical conditions. METHODS: A cross-sectional study was conducted in October 2005 in the outpatient clinics of King Khalid University Hospital in Riyadh, Kingdom of Saudi Arabia. A self-administered questionnaire was used for data collection. The statistical package for Social Science was used for analysis. RESULTS: Five hundred and one patients were included in the study. The mean age was 45.6 +/- 16.8. Fifty-five percent were female and 29% were highly educated. Most of the patients (64.1%) knew their diagnoses. This was significantly associated with the educational level; chronicity of the disease, and the awareness of other issues related to their illness such as complications and name of their medications (p<0.05). Few patients (20%) knew complications of their diseases. Seventy percent of patients were satisfied with their doctors' explanation of their disease. Knowing the diagnosis (p=0.001) and the disease complications (p=0.014) were associated significantly with patients' satisfaction. CONCLUSION: These figures are less than what they should be. Physicians must be advised of the importance of proper patient education. In addition, the lack of proper education by physicians demonstrated in this study should be compensated for by an increase in non-physician based education tools.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".