Exploring medical and veterinary student perceptions and communication preferences related to antimicrobial resistance in Ontario, Canada using qualitative methods
Bibliographic record
Abstract
BACKGROUND: Antimicrobial resistance (AMR) threatens our ability to treat and prevent infectious diseases worldwide. A significant driver of AMR is antimicrobial use (AMU) in human and veterinary medicine. Therefore, education and awareness of AMR among antimicrobial prescribers is critical. Human and animal health professionals play important roles in the AMR issue, both as contributors to the emergence of AMR, and as potential developers and implementers of effective solutions. Studies have shown that engaging stakeholders prior to developing communication materials can increase relevance, awareness, and dissemination of research findings and communication materials. As future antimicrobial prescribers, medical and veterinary students' perspectives on AMR, as well as their preferences for future communication materials, are important. The first objective of this study was to explore medical and veterinary student perceptions and understanding of factors associated with emergence and spread of AMR. The second objective was to identify key messages, knowledge translation and transfer (KTT) methods, and dissemination strategies for communication of AMR information to these groups. METHODS: Beginning in November 2018, focus groups were conducted with medical and veterinary students in Ontario, Canada. A semi-structured format, using standardized open-ended questions and follow-up probing questions was followed. Thematic analysis was used to identify and analyze patterns within the data. RESULTS: Analyses showed that students believed AMR to be an important global issue and identified AMU in food-producing animals and human medicine as the main drivers of AMR. Students also highlighted the need to address society's reliance on antimicrobials and the importance of collaboration between different sectors to effectively reduce the emergence and transmission of AMR. When assessing different communication materials, students felt that although infographics provide easily digestible information, other KTT materials such as fact sheets are better at providing more information without overwhelming the target audiences (e.g., professional or general public). CONCLUSION: Overall, the study participants felt that AMR is an important issue and emphasized the need to develop different KTT tools for different audiences. This research will help inform the development of future communication materials, and support development of AMR-KTT tools tailored to the needs of different student and professional groups.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".