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Record W4391487769 · doi:10.1177/23743735241229384

Patients’ Experiences of a Precision Medicine Clinic

2024· article· en· W4391487769 on OpenAlexafffund
David Barrett, Jovana Sibalija, Richard B. Kim

Bibliographic record

VenueJournal of Patient Experience · 2024
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsMedicineFeelingFamily medicineHealth carePsychology

Abstract

fetched live from OpenAlex

The purpose of this study is to provide an overview of patients' experiences using a precision medicine (PM) clinic that conducts pharmacogenomics-based (PGx) testing for adverse drug reactions. The study aimed to identify the features of the clinic valued most by patients and areas for improvement. A paper survey was used to collect data. Survey questions focused on patients' perceptions of the PM testing and the overall clinic experience. Sixty-seven patients completed the survey. Quantitative data were analyzed using SPSS and frequencies were reported. Open-ended responses were coded and organized thematically. Patients reported that the clinic services increased confidence in their medication usage. Feeling respected by staff, receiving education, and quick appointments were highly valued by patients. Suggested areas for improvement included better communication from the clinic to patients, expansion of clinic services, and education for other healthcare providers. The findings demonstrate that patient experience goes beyond the clinical care provided. Current and potential future providers of PM should invest the time and energy to configure their care delivery system to enhance the patient experience.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0030.000

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 head0.112
GPT teacher head0.481
Teacher spread0.369 · 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.

Study designNot applicable
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
Published2024
Admission routes2
Has abstractyes

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