Health care professionals' knowledge and attitudes towards antibiotic prescribing for the treatment of urinary tract infections: A systematic review
Bibliographic record
Abstract
PURPOSE: Previous models identify knowledge and attitudes that influence prescribing behaviour. The present study focuses on antibiotic prescribing for urinary tract infections (UTIs) to describe levels of health care professionals' knowledge and attitude factors in this area and how those levels are assessed. METHODS: A systematic search was conducted to identify studies assessing the identified knowledge or attitude factors influencing health care professionals' antibiotic prescribing for urinary tract infections up to September 2022. Study quality was assessed using the Newcastle-Ottawa scale. Data were extracted about the types of factors assessed, the levels indicated and how those levels were assessed. Data were synthesized using counts, and levels were categorized as 'poor', 'moderate', 'high' or 'very high'. RESULTS: Seven studies were identified, six of which relied entirely on closed-ended items. Levels of knowledge factors assessed were poor, for example, their 'knowledge of condition' and 'knowledge of task environment' were poor. Levels of the attitude factors assessed varied, for example, while health care professionals expressed moderate confidence in providing optimal patient care and appropriate attitude of fear towards the problem of antibiotic resistance, they expressed a poor attitude of complacency by giving into patient pressure to prescribe an antibiotic. CONCLUSIONS: Present evidence suggests that clinicians have poor levels of knowledge and varying levels of attitudes about antibiotic prescribing for UTIs. However, few studies were identified, and assessments were largely limited to closed-ended types of questions. Future studies that assess more factors and employ open-ended question types could better inform future interventions to optimize antibiotic prescribing.
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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 | 0.010 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".