Factors Affecting Pre-Travel Health Seeking Behaviour and Adherence to Pre-Travel Health Advice: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Recent years have seen unprecedented growth in international travel. Travellers are at high risk for acquiring infections while abroad and potentially bringing these infections back to their home country. There are many ways to mitigate this risk by seeking pre-travel advice (PTA), including receiving recommended vaccinations and chemoprophylaxis, however many travellers do not seek or adhere to PTA. We conducted a systematic review to further understand PTA-seeking behaviour with an ultimate aim to implement interventions that improve adherence to PTA and reduce morbidity and mortality in travellers. METHODS: We conducted a systematic review of published medical literature selecting studies that examined reasons for not seeking PTA and non-adherence to PTA over the last ten years. 4484 articles were screened of which 56 studies met our search criteria after full text review. RESULTS: The major reason for not seeking or non-adherence to PTA was perceived low risk of infection while travelling. Side effects played a significant role for lack of adherence specific to malaria prophylaxis. CONCLUSIONS: These data may help clinicians and public health providers to better understand reasons for non-adherence to PTA and target interventions to improve travellers understanding of potential and modifiable risks. Additionally, we discuss specific recommendations to increase public health education that may enable travellers to seek PTA.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".