A behavioural approach to specifying interventions: what insights can be gained for the reporting and implementation of interventions to reduce antibiotic use in hospitals?
Bibliographic record
Abstract
BACKGROUND: Reducing unnecessary antibiotic exposure is a key strategy in reducing the development and selection of antibiotic-resistant bacteria. Hospital antimicrobial stewardship (AMS) interventions are inherently complex, often requiring multiple healthcare professionals to change multiple behaviours at multiple timepoints along the care pathway. Inaction can arise when roles and responsibilities are unclear. A behavioural perspective can offer insights to maximize the chances of successful implementation. OBJECTIVES: To apply a behavioural framework [the Target Action Context Timing Actors (TACTA) framework] to existing evidence about hospital AMS interventions to specify which key behavioural aspects of interventions are detailed. METHODS: Randomized controlled trials (RCTs) and interrupted time series (ITS) studies with a focus on reducing unnecessary exposure to antibiotics were identified from the most recent Cochrane review of interventions to improve hospital AMS. The TACTA framework was applied to published intervention reports to assess the extent to which key details were reported about what behaviour should be performed, who is responsible for doing it and when, where, how often and with whom it should be performed. RESULTS: The included studies (n = 45; 31 RCTs and 14 ITS studies with 49 outcome measures) reported what should be done, where and to whom. However, key details were missing about who should act (45%) and when (22%). Specification of who should act was missing in 79% of 15 interventions to reduce duration of treatment in continuing-care wards. CONCLUSIONS: The lack of precise specification within AMS interventions limits the generalizability and reproducibility of evidence, hampering efforts to implement AMS interventions in practice.
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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 | 0.464 | 0.606 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".