Prevalence of Self-Medication in Dental Patients: A Case Study of Saudi Arabia
Bibliographic record
Abstract
The present study focuses on the prevalence of self-medication in dental patients’ pre-post dental consultation- a case study of Saudi Arabia. It was a descriptive study based on a structured close-ended interviewer-administered questionnaire. The questionnaire consisted of socio-demographic characteristics and also encompassed reasons, sources, duration, types of medicines used for self-medication, and reasons for hesitancy towards dental consultation. Respondents were selected using a non-probability convenience sampling technique, the data were analyzed using SPSS ver 22. Outcomes of the present study envisage that self-medication is quite prevalent among dental patients using both orthodox and traditional drugs. Results of Bivariate analysis revealed that the majority of patients were not cognizant of the specific dental ailments as revealed in pre-post diagnosis. The multivariate technique of decision trees exhibited that two groups of patients need to be focused on regarding self-medication – those who are less than 20 years of age and Non-Saudi Arabic speakers who are more than 20 years of age. The results of the present study can form the basis for framing future policies for easy accessibility of dental consultation to the populace which may result in containment of self-medication within the Saudian context.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".