Nursing-led strategy to combat antimicrobial resistance: multi-method design
Bibliographic record
Abstract
BACKGROUND: Antimicrobial resistance is currently a global health threat. Numerous efforts have been made to prevent or mitigate this phenomenon, but all have been insufficient. Although numerous studies have postulated nursing as a potential mediator to address this problem, no research has been conducted to put it into practice. This study aims to contextualize the problem of inappropriate antibiotic use and antimicrobial resistance, as well as to develop and implement a nurse-led educational intervention aimed at mitigating it, emphasizing the role of nurses in health education. METHODS: Two different groups of participants were recruited using convenience sampling. Nurses administered a survey to 782 citizens in a population from Spain to assess their knowledge of the correct use of antibiotics and antimicrobial resistance. After completing the survey, nurses explained to the participants how to use antibiotics correctly and the problem of antimicrobial resistance. Furthermore, an educational intervention led by nurses was carried out with 104 adolescents, consisting of an oral presentation to raise awareness about the issue. The effectiveness of this intervention was evaluated through a comparative analysis before and after the activity (pre-test and post-test). RESULTS: Our results indicated that the level of knowledge about the correct use of antibiotics and antimicrobial resistance is not statistically significant related to sex in the general population (p > 0.05). However, it is statistically significant related to age (p < 0.05), educational level (p < 0.0001) and study area (p < 0.0001). In addition, the nurse-led educational intervention increased significantly the level of knowledge on the topic among adolescents (p < 0.0001). CONCLUSIONS: These findings highlight the low level of knowledge in the population about the correct use of antibiotics and antimicrobial resistance. It also demonstrates how nurses, through their role in health education, can actively contribute to addressing the issue, providing a rationale for the inclusion of nursing in the design and implementation of strategies to prevent or mitigate antimicrobial resistance.
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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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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; 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".