Physician-reported barriers to using evidence-based antibiotic prescription guidelines in primary care: protocol for a systematic review and synthesis of qualitative studies using the Theoretical Domains Framework
Bibliographic record
Abstract
INTRODUCTION: Overprescription of antibiotics poses a significant threat to healthcare globally as it contributes to the issue of antibiotic resistance. While antibiotics should be predominately prescribed for bacterial infections, they are often inappropriately given for uncomplicated upper respiratory tract infections (URTIs) and related conditions, such as the common cold. This study will involve a qualitative systematic review of physician-reported barriers to using evidence-based antibiotic prescription guidelines in primary care settings and synthesise the findings using a theoretical basis. METHODS AND ANALYSIS: We will conduct a systematic review of qualitative studies that assess physicians' reported barriers to following evidence-based antibiotic prescription guidelines in primary care settings for URTIs. We plan to search the following databases with no date or language restrictions: MEDLINE, Web of Science, CINAHL, Embase, the Cochrane Library and PsycInfo. Qualitative studies that explore the barriers and enablers to following antibiotic prescription guidelines for URTIs for primary care physicians will be included. We will analyse our findings using the Theoretical Domains Framework (TDF), which is a theoretically designed resource based on numerous behaviour change theories grouped into 14 domains. Using the TDF approach, we will be able to identify the determinants of our behaviour of interest (ie, following antibiotic prescription guidelines for URTIs) and categorise them into the 14 TDF domains. This will provide the necessary information to develop future evidence-based interventions that will target the identified issues and apply the most effective behaviour change techniques to affect change. This protocol follows the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols guidelines. ETHICS AND DISSEMINATION: Ethical approval is not required. Findings will be published in a peer-reviewed journal and presented at conferences.
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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 | 0.155 | 0.157 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.016 | 0.018 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.062 | 0.008 |
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".