Teenagers and Precision Psychiatry: A Window of Opportunity
Bibliographic record
Abstract
OBJECTIVE: Precision medicine raises hope for translating genetic-based knowledge about psychiatric risks into mental health benefits by motivating health-related, risk-reducing behaviors. Teenagers (ages 14-17) are an important age-group to engage in preventive efforts but, their views about psychiatric genetics are understudied. METHOD: An online survey with a nationally representative sample of teenagers (n = 417) was conducted. Participants were randomly assigned to receive 1 of 2 handouts, 1 emphasizing the genetic underpinnings of psychiatric conditions; the other agency-oriented and focusing on gene-environment interactions. Survey questions queried their views about behavioral changes in response to psychiatric genetic risk information and expressed willingness to undertake them. Participants' decision-making characteristics (i.e., self-efficacy, empowerment, intolerance of uncertainty, and sensation-seeking) were assessed at baseline. RESULTS: Teenagers strongly valued the information provided and its potential usefulness for their mental health. Information about psychiatric genetics alone impacted views about the causes of mental illness. Contrary to our hypothesis, the type of handout did not impact participants' expressed willingness to make behavioral changes to reduce their risk of developing a psychiatric condition, but their sense of empowerment played a key role in their responses. CONCLUSION: Educating teenagers about gene-environment interactions may help facilitate the translational efforts of precision psychiatry. Research with teenagers across racial/ethnic groups, especially those with family histories, is needed to better understand the factors that impact teenagers' empowerment in psychiatric genomic settings and to identify measures, including the best enablers of empowerment (e.g., educators, parents), which would allow them to reap the benefits of precision psychiatry.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".