User Experiences of Pharmacogenomic Testing and Opinions among Psychiatry Patients
Bibliographic record
Abstract
Pharmacogenomic testing (PGx) is a tool used to guide physicians in selecting an optimal medication for clients based on their genetic profile. The objective of this qualitative study is to understand patients' experiences with PGx testing as well as their opinions regarding the clinical adoption of such tests in psychiatry. A focus group was conducted to assess the needs of clients who had experience using a PGx test. Participants were recruited from a large study on PGx testing that offered physicians an opportunity to use PGx reports to guide psychotropic prescriptions. The focus group discussions were recorded, transcribed, and coded using NVivo to identify core themes. A total of 11 people participated in the focus group. Our analysis revealed that many participants were in favour of implementing PGx testing in psychiatric practice, and all expressed important considerations for patient-centred optimization of PGx testing. The main themes captured were: education and awareness among clinicians, cost considerations, PGx results-sharing and accessibility, and prospective benefits. The results of this study suggest that patients are keen to see PGx testing in widespread clinical care, but they report important opportunities to improve knowledge mobilization of PGx testing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".