Italian Medical Professionals' Practices, Attitudes, and Knowledge in Travel Medicine: Protocol for a National Survey
Bibliographic record
Abstract
BACKGROUND: The evolving global health landscape highlights the importance of travel medicine, making it necessary for health care professionals to understand the epidemiologic profiles among varied traveler populations and keep themselves updated in this rapidly changing field. However, in Italy, travel medicine clinics have significant gaps in resource allocation, staff training, and infrastructure. OBJECTIVE: This protocol of a cross-sectional study aims to create and validate a questionnaire to assess the knowledge, attitudes, and practices of health care professionals in travel medicine in Italy. The final goal is to provide a tool to evaluate the state of travel medicine, guide training initiatives, and be able to monitor trends over time. METHODS: The study population consists of health care professionals who practice travel medicine in Italy. The questionnaire will be developed by adapting an existing English survey and conducting a scoping review to align the questionnaire with contemporary scientific discourse. The validation process includes face validity, content validity, and expert evaluation. The sample size, determined through power analysis, ranges from 218 to 278 participants. The questionnaire will undergo a pilot test on a smaller sample size (10% of the total) to identify and address any issues. Statistical analysis will include central tendency and dispersion measures, categorical summaries, group comparisons, and regressions. This research received ethical approval, and informed consent will be obtained from all participants. RESULTS: As of July 2024, we completed the questionnaire validation involving 9 experts. The validated version of the questionnaire includes 86 items. Furthermore, we conducted a pilot test on 53 individuals during the SIMVIM (Italian Society of Travel Medicine and Migrations) course on travel medicine held in Lucca, Italy, on June 14, 2024. CONCLUSIONS: This cross-sectional study will guide strategic planning and targeting training and awareness activities in areas deemed most critical or lacking. The study's structured approach and periodic assessments will facilitate the identification of educational gaps, the dissemination of best practices, and the overall improvement of health care services for travelers in Italy. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/59511.
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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 | 0.029 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.011 |
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".