A recipe for antimicrobial stewardship success: Using intervention mapping to develop a program to reduce antibiotic overuse in long-term care
Bibliographic record
Abstract
OBJECTIVE: To better understand barriers and facilitators that contribute to antibiotic overuse in long-term care and to use this information to inform an evidence and theory-informed program. METHODS: Information on barriers and facilitators associated with the assessment and management of urinary tract infections were identified from a mixed-methods survey and from focus groups with stakeholders working in long-term care. Each barrier or facilitator was mapped to corresponding determinants of behavior change, as described by the theoretical domains framework (TDF). The Rx for Change database was used to identify strategies to address the key determinants of behavior change. RESULTS: In total, 19 distinct barriers and facilitators were mapped to 8 domains from the TDF: knowledge, skills, environmental context and resources, professional role or identity, beliefs about consequences, social influences, emotions, and reinforcements. The assessment of barriers and facilitators informed the need for a multifaceted approach with the inclusion of strategies (1) to establish buy-in for the changes; (2) to align organizational policies and procedures; (3) to provide education and ongoing coaching support to staff; (4) to provide information and education to residents and families; (5) to establish process surveillance with feedback to staff; and (6) to deliver reminders. CONCLUSIONS: The use of a stepped approach was valuable to ensure that locally relevant barriers and facilitators to practice change were addressed in the development of a regional program to help long-term care facilities minimize antibiotic prescribing for asymptomatic bacteriuria. This stepped approach provides considerable opportunity to advance the design and impact of antimicrobial stewardship programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".