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Record W2803614538 · doi:10.1093/jtm/tay032

The changing landscape of travel health services in Canada

2018· article· en· W2803614538 on OpenAlexafffundabout
Yen‐Giang Bui, Susan Kuhn, Mariama Sow, Anne McCarthy, J Geduld, François Milord

Bibliographic record

VenueJournal of Travel Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicTravel-related health issues
Canadian institutionsPublic Health Agency of CanadaUniversity of OttawaWilfrid Laurier UniversityUniversité de SherbrookeInstitut National de Santé Publique du QuébecSanté MontérégieCentre intégré de santé et de services sociaux de Chaudière-AppalachesOttawa HospitalUniversity of CalgaryCentre intégré de santé et de services sociaux de la Montérégie-CentreAlberta Children's HospitalCentre Intégré de Santé et de Services Sociaux des Laurentides
FundersInstitut National de Santé Publique du QuébecPublic Health AgencyPublic Health Agency of Canada
KeywordsMedicinePharmacyCompetence (human resources)AutonomyHealth servicesFamily medicineNursingEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

Canadians are increasingly travelling to areas that would necessitate a pre-travel consultation. Changes in professional regulations in Canada allow greater autonomy of nurses and pharmacists, resulting in shifts in provision of travel health services. We surveyed 824 Canadian travel clinics, 270 (33%) of whom responded. Private clinics were most common, and more likely to offer extended hours and drop-in appointments. In one province, pharmacies dominated. Half the services were relatively new and a similar proportion saw fewer than 10 patients weekly; 1/3 had a single provider. The increased spectrum of services may increase convenience for travellers but the large proportion seeing low numbers of clients will challenge providers to maintain competence.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.309
Teacher spread0.289 · 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

Citations8
Published2018
Admission routes3
Has abstractyes

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