MétaCan
Menu
Back to cohort
Record W3129413531 · doi:10.2217/pgs-2020-0077

Sociodemographic Factors and Beliefs About Medicines in the Uptake of Pharmacogenomic Testing in Older Adults

2021· article· en· W3129413531 on OpenAlexafffund
Alexandra Chapdelaine, Catherine Lamoureux‐Lamarche, Thomas G. Poder, Helen‐Maria Vasiliadis

Bibliographic record

VenuePharmacogenomics · 2021
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsInstitut universitaire en santé mentale de MontréalInstitut Universitaire en Santé Mentale de QuébecUniversité de MontréalUniversité de Sherbrooke
FundersCanadian Institutes of Health Research
KeywordsOddsOdds ratioMedicinePharmacogenomicsSample (material)Family medicineGerontologyDemographyLogistic regressionInternal medicinePharmacology

Abstract

fetched live from OpenAlex

Aim: To assess the impact of sociodemographic factors and beliefs about medicines on the uptake of pharmacogenomic testing in older adults in a public healthcare system. Materials & methods: Data are based on a sample of 347 primary care older adults. Results: Most respondents (90%) were willing to provide a saliva sample and 47% were willing to pay for it. Increased age (odds ratio: 0.91; p = 0.04) and negative beliefs about the harmfulness of medicines (odds ratio: 0.68; p = 0.02) were associated with a decreased willingness to provide a sample. Lower education (less than university, odds ratio: 0.54; p = 0.04) was associated with a decreased willingness to pay. Conclusion: Education and beliefs about medicines are important factors in the acceptability of pharmacogenomic testing in older adults.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.068
GPT teacher head0.362
Teacher spread0.294 · 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

Citations7
Published2021
Admission routes2
Has abstractyes

Explore more

Same venuePharmacogenomicsSame topicPharmaceutical studies and practicesFrench-language works237,207