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Record W2751940288 · doi:10.3233/978-1-61499-794-8-147

A Comparison of Danish and Canadian Consumer Medication Information

2017· article· en· W2751940288 on OpenAlexaffabout
Helen Monkman, André Kushniruk

Bibliographic record

VenueStudies in health technology and informatics · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDanishMedical prescriptionMedicineResource (disambiguation)Resource useFamily medicineNursingComputer science

Abstract

fetched live from OpenAlex

Many people around the world use prescription medications. Consumers often require information about their medications to support taking them safely and effectively. One source of such information is Consumer Medication Information (CMI). Canadians typically receive printed CMI when a new prescription is filled whereas Danes have the online resource min.medicin.dk. This study compared the content and design of Danish and Canadian CMI. Danish CMI satisfied seven of the 11 content utility criteria (developed in previous work) identified as supporting the safe and effective medication use. However, Danish CMI provided a more information about how frequently possible side effects occur and multimedia (e.g., images, videos) directions for some medications. This study examined some of the similarities and differences between how Canadians and Danes are informed about medications. However, further research is required to determine what content and methods of delivery are most beneficial in supporting safe and effective medication use.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.046
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.008
Science and technology studies0.0050.001
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.128
GPT teacher head0.536
Teacher spread0.408 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2017
Admission routes2
Has abstractyes

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