The use of theory in the development and evaluation of behaviour change interventions to improve antimicrobial prescribing: a systematic review
Bibliographic record
Abstract
OBJECTIVES: This systematic review (SR) reviews the evidence on use of theory in developing and evaluating behaviour change interventions (BCIs) to improve clinicians' antimicrobial prescribing (AP). METHODS: The SR protocol was registered with PROSPERO. Eleven databases were searched from inception to October 2018 for peer-reviewed, English-language, primary literature in any healthcare setting and for any medical condition. This included research on changing behavioural intentions (e.g. in simulated scenarios) and research measuring actual AP. All study designs/methodologies were included. Excluded were: grey literature and/or those which did not state a theory. Two reviewers independently extracted and quality assessed the data. The Theory Coding Scheme (TCS) evaluated the extent of the use of theory. RESULTS: Searches found 4227 potentially relevant papers after removal of duplicates. Screening of titles/abstracts led to dual assessment of 38 full-text papers. Ten (five quantitative, three qualitative and two mixed-methods) met the inclusion criteria. Studies were conducted in the UK (n = 8), Canada (n = 1) and Sweden (n = 1), most in primary care settings (n = 9), targeting respiratory tract infections (n = 8), and medical doctors (n = 10). The most common theories used were Theory of Planned Behaviour (n = 7), Social Cognitive Theory (n = 5) and Operant Learning Theory (n = 5). The use of theory to inform the design and choice of intervention varied, with no optimal use as recommended in the TCS. CONCLUSIONS: This SR is the first to investigate theoretically based BCIs around AP. Few studies were identified; most were suboptimal in theory use. There is a need to consider how theory is used and reported and the systematic use of the TCS could help.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".