Barriers and facilitators of implementing an antimicrobial stewardship intervention for urinary tract infection in a long-term care facility
Bibliographic record
Abstract
Background: Fifty percent of antibiotic courses in long-term care facilities (LTCFs) are unnecessary, leading to increased risk of harm. Most studies to improve antibiotic prescribing in LTCFs showed modest and unsustained results. We aimed to identify facilitators, barriers and strategies in implementing a urinary tract infection (UTI)–focused antimicrobial stewardship (AS) intervention at a LTCF, with the secondary objective of exploring the pharmacist’s potential roles. Methods: The study used a qualitative descriptive design. Participants attended either a focus group or one-on-one interview. Data were analyzed inductively using a codebook modified in an iterative analytic process. Barrier and facilitator themes were mapped using the capability, opportunity, motivation and behaviour (COM-B) model. Similarly, themes were identified from the transcripts regarding the pharmacist’s roles. Results: Sixteen participants were interviewed. Most barriers and facilitators mapped to the opportunities domain of the COM-B model. The main barrier themes were lack of access, lack of knowledge, ineffective communication, lack of resources and external factors, while the main facilitator themes were education, effective collaboration, good communication, sufficient resources and access. For the pharmacist’s role, the barrier themes were ineffective collaboration and communication. Conclusion: This study supports the importance of tailoring interventions to target factors underlying barriers to behaviour change. At this LTCF, an effective antimicrobial stewardship intervention should incorporate strategies to improve access, knowledge, communication and collaboration in its design, having sufficient resources and addressing external factors to optimize its success and long-term sustainability. Can Pharm J (Ott) 2021;154:xx-xx.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".