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Travelers’ Knowledge of Prevention and Treatment of Travelers’ Diarrhea

2006· article· en· W2054669941 on OpenAlexaffabout
Julie Y.M. Johnson, Lynn M. McMullen, Paul Hasselback, Marie Louie, L. Duncan Saunders

Bibliographic record

VenueJournal of Travel Medicine · 2006
Typearticle
Languageen
FieldMedicine
TopicTravel-related health issues
Canadian institutionsProvincial Laboratory of Public HealthInterior HealthUniversity of Alberta
Fundersnot available
KeywordsMedicineTraveler's diarrheaDiarrheaEnvironmental healthTravel medicineThe InternetPublic healthFamily medicineNursingPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Information regarding the prevention and treatment of travelers' diarrhea (TD) is available to the public from various sources, such as medical personnel, travel clinics, personal contacts, and the Internet. This type of information may help travelers avoid this illness or help those afflicted minimize its duration. METHODS: We collected questionnaire data from 104 travelers at departure gates for flights to Mexico from Calgary, Alberta on their knowledge of symptoms and treatment of TD and food risks associated with this illness and sources of information used. RESULTS: Almost half reported they received some information on travel-related diseases and on TD prior to the flight. When education level was controlled for, the mean score for people who had obtained information on TD was significantly higher than that for those who did not have such information. College or university-educated travelers scored better than did other travelers. A high proportion of travelers correctly identified risk levels associated with specific foods consumed during travel, and many recognize that they are at an increased risk of acquiring diarrheal illness while traveling in a developing country. CONCLUSIONS: Information on TD appears to improve the level of knowledge on its prevention and treatment among travelers from southern Alberta.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.341
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2006
Admission routes2
Has abstractyes

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