Exploring dentist’s engagement with antibiotic stewardship programs: a multicenter qualitative study in Pakistan
Bibliographic record
Abstract
Introduction: Antibiotic resistance is a growing threat to public health. Improper use of antibiotics can potentially lead to previously curable infections becoming unmanageable. Rampant unnecessary use of antibiotics is a serious problem within developing countries like Pakistan, and dental practices can often be traced as a major cause. Through the widespread implementation of antibiotic stewardship programs (ASP), medical and dental practitioners can be guided to appropriate prescribing practices and better patient education. Objective: To gauge current antimicrobial prescribing practices of dentists and their engagement with ASPs. Methods: 13 dental practitioners were interviewed using a semi-structured interview template. Interviews were recorded, transcribed, and subjected to thematic analysis. The subjects were selected from various dental hospitals within Punjab, using a convenient sampling approach. The sample size was based on the point of saturation of emerging themes. Results: Many factors were highlighted as causative for the current state of misinformation regarding antibiotic prescription practices among dental practitioners and patients. Awareness regarding ASP was severely lacking; however, most participants showed a positive perception regarding ASP and their impact. Institutional support and the need for implementation of such programs within the educational curriculum were noted as important steps for ASP implementation within dental hospitals of Pakistan. Conclusion: This study found that knowledge regarding ASP was insufficient among dental practitioners, owing to a lack of institutional policies and awareness among the practitioners. The outcome of the lack of these programs and misinformation among both patients and dentists is widely contributing to the current state of antibiotic resistance in Pakistan.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".