Pharmacogenetics and Genomics Implementation in Youth Mental Health: An International Policy Scoping Review
Bibliographic record
Abstract
Background: Pharmacogenetics (PGx) offers the potential to personalize mental health treatments by tailoring medication selection and dosing to an individual’s genetic profile. However, despite its growing scientific promise, policies governing the integration of PGx testing in mental health for young people remain fragmented. This research will examine international pharmacogenetic and genomic policies related to the psychotropic treatment in youths Method: A scoping review will be employed to analyze and integrate existing policies from selected international comparator countries. The scoping review will follow the Joanna Briggs Institute (JBI) guidelines for scoping reviews, and the reporting will follow the PRISMA-ScR guidelines. Peer-reviewed and grey literature will be searched across databases (MEDLINE, Scopus, Embase, APA PsycINFO, CINAHL, and Web of Science) and policy repositories (Policy Commons, Canada Commons, Overton, Dimensions, and Euro-PharmacoGenomics) from 2005 onward. Documents will be included if they address PGx-related policies, frameworks, or ethical guidelines for individuals aged 0–25 with mental health conditions. Data analysis will be a hybrid inductive–deductive thematic analysis in NVivo 15, guided by the Genomics and Ethics, Environmental, Economic, Legal, and Social (GE³LS) framework. Expected Outcomes: The research will identify leading international mental health policy strategies and innovative policy frameworks and deployment approaches that Canadian policymakers in Canada can apply to improved pharmacogenetics implementation. The research will develop evidence-based recommendations together with detailed implementation plans to achieve equal access to personalized mental health care for children and young people while backing international initiatives for responsible genomic innovation and individualized healthcare.
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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 | 0.079 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.017 | 0.029 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".