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Record W2942855427 · doi:10.3390/pharmacy7020042

Pharmacy Travel Health Services in Canada: Experience of Early Adopters

2019· article· en· W2942855427 on OpenAlexaffabout
Doug Thidrickson, Larry Goodyer

Bibliographic record

VenuePharmacy · 2019
Typearticle
Languageen
FieldMedicine
TopicTravel-related health issues
Canadian institutionsWinnipeg Regional Health Authority
Fundersnot available
KeywordsPharmacyPharmacistLegislationBusinessFamily medicineMedicineCompetence (human resources)Public healthService (business)Public relationsNursingMarketingPsychologyPolitical science

Abstract

fetched live from OpenAlex

Since 2007, community pharmacists in Canada have become increasingly involved in delivering Travel Health services, including the recommendation and administration of vaccines. This qualitative scoping survey examines some of the activities and opinions of those early pharmacist adopters delivering these services. A Survey Monkey free text questionnaire was emailed to pharmacists who were involved in delivering travel medicine services. 21 pharmacists responding represented seven Canadian provinces. Only 5 pharmacists estimated that they were seeing five or more patients a week on average. Amongst the challenges they faced the most quoted was lack of time when running a busy pharmacy (62%) a lack of prescribing authority, (52%), and lack of access to public health vaccines (52%). 'Word of mouth' was widely quoted as a means of developing the service, indicating a good patient satisfaction. Also expressed were the advantages of convenience in terms of being a 'one stop shop', ease of billing to insurance companies and convenient appointment times. There are a number of challenges which are still to be faced which may be resolved by further legislation allowing access to public health vaccines and more widespread prescribing rights. The relatively low level of consultations reported by some is of concern if those pharmacists are 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)0.000
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 score0.657
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

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

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 head0.363
Teacher spread0.325 · 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
Published2019
Admission routes2
Has abstractyes

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